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Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (April 27, 2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
Повний текст джерелаАнотація:
Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates ‘continuous and categorical’ meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.
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Dohlman, Anders B., and Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference." Experimental Biology and Medicine 244, no. 6 (March 16, 2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.
Повний текст джерелаАнотація:
Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome’s role in disease, more precisely predict the microbiome’s response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease. Impact statement This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome’s role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.
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Ramos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov, and Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.
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Abstract Recent studies have demonstrated that, despite their nomenclature, gliomas recapitulate an interneuron progenitor-like state that drives tumor progression. During human neurodevelopment, interneurons arise from the subcortical ganglionic eminences and migrate tangentially into the neocortex, settling in the cortical plate where they integrate local neurocircuitry. Analogously, malignant glioblastoma (GBM) cells migrate from the tumor core into the surrounding healthy tissue. This innate infiltrative property renders these malignant cells elusive to surgical resection, leading to tumor recurrence. To understand the regulatory networks that drive tumor infiltration from a neurodevelopmental perspective, we generated a single-nucleus Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) dataset of 41,000 nuclei from the core and infiltrative edge of surgically resected GBM specimens (n = 4). Concurrently, we sequenced 46,000 nuclei from non-pathological, postmortem samples of second- and third-trimester neocortices (n = 17). We integrated these datasets with paired single-nucleus RNA sequencing (snRNA-seq) data and identified candidate regulatory TFs that exhibit high correlation between motif enrichment and TF expression. Using single-trajectory inference and pseudo-time analyses, we identified TCF12 as a potential driver of interneuron lineage fate in developing cortical progenitors. Given its implication in projection neuron migration, we were intrigued to find that TCF12 activity is highest in GBM cells with a migratory interneuron signature, hinting at its putative role in tumor infiltration. To understand the significance of these findings, we will interrogate other genes in the TCF12 regulatory network with the ultimate goal of identifying therapeutic targets that inhibit GBM infiltration.
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Grund, Eric M., A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.
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Abstract Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for pancreatic ductal adenocarcinoma (PDAC). PDAC is expected to be the second leading cause of cancer death and has a dismal 5 year survival rate of 10%. There is an urgent unmet need to detect the disease at an early stage and stratify patients into more effective treatment regimens within clinically meaningful timeframes. To accomplish this, robust quality controlled OMIC molecular profiling platforms and analytic solutions need to be deployed into precision medicine protocols to discover actionable biomarkers. Project Survival® is a multicenter (n=6), prospective biomarker study (NCT 02781012) of PDAC and relevant controls combining high-fidelity longitudinal phenotypic characterization, multi-omic profiling (proteomics, signaling lipidomics, structural lipidomics, and metabolomics), and agnostic Bayesian artificial intelligence network inference (bAIcis®) to discover biomarkers with diagnostic and therapeutic utility. This study utilizes a systems medicine approach for translational biomarker discovery by performing analysis of matched subject sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissues and integrating them with the respective full clinical annotation using the BERG Interrogative Biology® platform. Multiple longitudinal time points were taken over the course of the six-year timeline enabling dynamic modeling. Utilizing the Project Survival® molecular and clinical data, we have analyzed and integrated baseline samples from 121 at risk patients and 279 patients with PDAC. Samples were randomized and analyzed over the course of recruitment allowing for agnostic discovery and integration to determine diagnostic utility. Discovery analysis identified 123 potential molecular markers, of which, four demonstrated a combined AUC of 0.85, PPV 0.83, NPV 0.72, and OR 13.1. In parallel, 4 non-canonical clinical measurements were assessed for diagnostic utility providing an AUC 0.79, PPV 0.84, NPV 0.72 and OR 13.2. Combining molecular and clinical features demonstrated an AUC of 0.9, PPV 0.9, NPV 0.77, OR 29.2, and p-value 1.4 E-40. Molecular markers revealed no treatment associated expression effects. Taken together, these marker panels demonstrate diagnostic utility to detect PDAC and will be further validated using robust bioanalysis methods as well as in an independent cohort of samples to provide enhanced insight into their positioning in the diagnostic landscape for PDAC. Citation Format: Eric M. Grund, A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, Valarie Bussberg, Vladimir Tolstikov, Rangaprasad Sarangarajan, Elder Granger, Niven Narian, Michael A. Kiebish. Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5145.
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Nathasingh, Brandon, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church, and Yaoyu E. Wang. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model." Cancer Research 83, no. 8_Supplement (April 14, 2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.
Повний текст джерелаАнотація:
Abstract Recent advances in artificial intelligence (AI) and availability of multimodal patient datasets have enabled the construction of complex network models to derive disease molecular mechanisms and predict the impact of therapeutic intervention. However, observational datasets are commonly affected by confounding factors making causal interpretation challenging. Causal inference network methods are particularly suited to facilitate therapeutic intervention studies by inferring the causal structure from sufficiently detailed multi-omic molecular data. The learned models enable in-silico loss-of-function screening experiments on patient data by using counterfactual simulation to reveal the impact of a gene loss in a disease model. These models enhance gene dependency characterization and the design of advanced therapeutic interventions. In this study, we developed an in-silico multiple myeloma (MM) patient causal model of overall survival (OS) based on transcriptomic expression, clinical, and genomic alteration data from Multiple Myeloma Research Foundation (MMRF) CoMMpass dataset (IA19). After filtering for data quality and availability, we included 516 patients, with 60% being hyperdiploid. We sampled Bayesian networks using Markov Chain Monte Carlo simulation to infer probabilistic causal relationships between network components that influence overall survival. We then simulated synthetic knock downs of those genes where a path exists to overall survival with posterior probability of at least 0.25. Next, we compared CRISPR-SpCas9 cancer dependency data from DepMap (version 22Q2) for multiple myeloma cell lines against genes predicted to be causal (causal genes) for overall survival. Last, we examined the causal genes that are non-dependent in MM cell lines for upstream genomic alterations to determine if specific patient genomic contexts are affecting the results. We identified 102 causal genes, including non-coding RNA genes (n=23), driving overall survival. Among them, 70% (56/79, p=2.2e-16, OR=9.5) of the coding genes were found to be MM-dependent in DepMap, with 44 common essential, 7 strongly selective and 5 weakly selective genes. From 23 genes identified as causal and not known to be MM-dependent, 20 (87%) were selective in other cancer lineages and all of them (23/23) had consistent upstream genomic alterations driving their expression. Causal genes identified from AI-driven in-silico experiments to predict overall survival were strongly enriched for known dependent genes from DepMap. Furthermore, we identified causal genes that may be dependent in unique patient genomic contexts. This demonstrates in-silico AI causal modelling is a powerful tool for exploring cancer cell vulnerability directly from patient data to advance target discovery. Citation Format: Brandon Nathasingh, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W. Church, Yaoyu E. Wang. Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB181.
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Ye, Qing, and Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets." Cells 12, no. 1 (December 26, 2022): 101. http://dx.doi.org/10.3390/cells12010101.
Повний текст джерелаАнотація:
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Alanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, Claire S. Simon, Afshan McCarthy, Kay Elder, Phil Snell, Leila Christie, and Kathy K. Niakan. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos." Life Science Alliance 7, no. 1 (October 25, 2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.
Повний текст джерелаАнотація:
Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.
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Wang, Pei. "Network biology: Recent advances and challenges." Gene & Protein in Disease 1, no. 2 (October 6, 2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.
Повний текст джерелаАнотація:
Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information mining in biological networks are reviewed. The principles of guilt-by-association and guilt-by-rewiring in network biology and their applications are discussed. Although great advances have been achieved in the field of network biology over the past decades, there are still many challenging issues. First, efficient and reliable network inference algorithms for high-dimensional and highly noisy omics data are still in great demand. Second, the research focus will be on multilayer biological network theory. This plays a critical role in the exploration of the multi-scale or dynamical characteristics of complex biomolecular networks by integrating multi-source heterogeneous omics data. Third, a close cooperation among biologists, medical workers, and researchers from network science is still a prerequisite in the applications of network biology. The rapid development of network biology will undoubtedly raise important clues for understanding complex phenotypes in biological systems.
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Yan, Yan, Feng Jiang, Xinan Zhang, and Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm." Entropy 24, no. 5 (May 13, 2022): 693. http://dx.doi.org/10.3390/e24050693.
Повний текст джерелаАнотація:
One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.
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Bonnet, Eric, Laurence Calzone, and Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree." PLOS Computational Biology 11, no. 2 (February 13, 2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.
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Wang, Huange, Joao Paulo, Willem Kruijer, Martin Boer, Hans Jansen, Yury Tikunov, Björn Usadel, Sjaak van Heusden, Arnaud Bovy, and Fred van Eeuwijk. "Genotype–phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes." Molecular BioSystems 11, no. 11 (2015): 3101–10. http://dx.doi.org/10.1039/c5mb00477b.
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Zarayeneh, Neda, Euiseong Ko, Jung Hun Oh, Sang Suh, Chunyu Liu, Jean Gao, Donghyun Kim, and Mingon Kang. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.087178.
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Kang, Mingon, Donghyun Kim, Jean Gao, Chunyu Liu, Sang Suh, Jung Hun Oh, Neda Zarayeneh, and Euiseong Ko. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.10008266.
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Hu, Xinlin, Yaohua Hu, Fanjie Wu, Ricky Wai Tak Leung, and Jing Qin. "Integration of single-cell multi-omics for gene regulatory network inference." Computational and Structural Biotechnology Journal 18 (2020): 1925–38. http://dx.doi.org/10.1016/j.csbj.2020.06.033.
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Peñagaricano, F. "S0101 Causal inference of molecular networks integrating multi-omics data." Journal of Animal Science 94, suppl_4 (September 1, 2016): 2. http://dx.doi.org/10.2527/jas2016.94supplement42a.
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Peñagaricano, F. "0412 Causal inference of molecular networks integrating multi-omics data." Journal of Animal Science 94, suppl_5 (October 1, 2016): 199–200. http://dx.doi.org/10.2527/jam2016-0412.
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Sun, Xiaoqiang, Ji Zhang, and Qing Nie. "Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples." PLOS Computational Biology 17, no. 3 (March 5, 2021): e1008379. http://dx.doi.org/10.1371/journal.pcbi.1008379.
Повний текст джерелаАнотація:
Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.
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Gao, Wenliang, Wei Kong, Shuaiqun Wang, Gen Wen, and Yaling Yu. "Biomarker Genes Discovery of Alzheimer’s Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia." Brain Sciences 12, no. 9 (September 5, 2022): 1196. http://dx.doi.org/10.3390/brainsci12091196.
Повний текст джерелаАнотація:
Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer’s disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC). This enables snRNA-seq data and bulkRNA-seq data to obtain data on the deeper intermolecular regulatory relationships, related genes, and the molecular mechanisms of immune-cell action. In our approach, not only were central transcription factors (TF) STAT3, CEBPB, SPI1, and regulatory mechanisms identified more accurately than with single-omics but also immunotherapy targeting central TFs to drugs was found to be significantly different between patients. Thus, in addition to providing new insights into the potential regulatory mechanisms and pathogenic genes of AD microglia, this approach can assist clinicians in making the most rational treatment plans for patients with different risks; it also has significant implications for identifying AD immunotherapy targets and targeting microglia-associated immune drugs.
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Federico, Anthony, Joseph Kern, Xaralabos Varelas, and Stefano Monti. "Structure Learning for Gene Regulatory Networks." PLOS Computational Biology 19, no. 5 (May 18, 2023): e1011118. http://dx.doi.org/10.1371/journal.pcbi.1011118.
Повний текст джерелаАнотація:
Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE—Structure Learning for Hierarchical Networks—a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.
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Cha, Junha, and Insuk Lee. "Single-cell network biology for resolving cellular heterogeneity in human diseases." Experimental & Molecular Medicine 52, no. 11 (November 2020): 1798–808. http://dx.doi.org/10.1038/s12276-020-00528-0.
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AbstractUnderstanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.
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Capobianco, Enrico. "Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology." Journal of Clinical Medicine 8, no. 5 (May 11, 2019): 664. http://dx.doi.org/10.3390/jcm8050664.
Повний текст джерелаАнотація:
Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes.
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Han, Xudong, Bing Wang, Chenghao Situ, Yaling Qi, Hui Zhu, Yan Li, and Xuejiang Guo. "scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data." PLOS Biology 21, no. 11 (November 13, 2023): e3002369. http://dx.doi.org/10.1371/journal.pbio.3002369.
Повний текст джерелаАнотація:
Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene–cell association network for inferring single-cell pathway activity scores and identifying cell phenotype–associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.
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Kim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim, and Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer." Bioinformatics 37, no. 16 (February 5, 2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.
Повний текст джерелаАнотація:
Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene–gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability and implementation iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. Supplementary information Supplementary data are available at Bioinformatics online.
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Vincent, Jonathan, Pierre Martre, Benjamin Gouriou, Catherine Ravel, Zhanwu Dai, Jean-Marc Petit, and Marie Pailloux. "RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data." PLOS ONE 10, no. 5 (May 19, 2015): e0127127. http://dx.doi.org/10.1371/journal.pone.0127127.
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Schneider, Nimisha, Sergey Korkhov, Alexis Foroozan, Scott Marshall, and Renee Deehan. "Causal inferencing of -omics data from The Cancer Genome Atlas: Lung adenocarcinoma tumors for mechanistic disease characterization and feature engineering." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e21016-e21016. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21016.
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e21016 Background: Advances in high throughput measurement technologies (-omics data) have made it possible to generate high complexity, high volume data for oncology research. Researchers are often confronted many more measurements than samples (p > > > n), which poses challenges for both modeling the complexity of disease at the molecular mechanism level, and overfitting when generating predictive models with complex data. Here, we applied a prior knowledge-driven approach to characterize and classify heavy versus light smokers with lung cancer from The Cancer Genome Atlas, an open source repository that catalogs, harmonizes and hosts -omics data collected from samples generously donated from cancer patients. Methods: We applied a reverse inferencing approach to systematically interrogate RNAseq measurements from tumor and control biopsies against a knowledgebase of directed gene networks curated from published experiments. If patterns observed in the data are significantly similar to those in a network, an inference about the directional activity of that network can be made; e.g., the increased transcriptional activity of NFKB. Our library was nucleated through an open sourced knowledge graph and enhanced with updated and relevant knowledge using the open sourced Biological Expression Language framework. Directed networks were either qualitatively scored and used to build disease models, or semi-quantitatively scored and used as classification features. Results: In LUAD tumors, we detected a pattern of gene signatures which indicated a tumor stem cell-like phenotype characterized by predicted decreases in the activity of pro-differentiation factors and an increased response to hypoxia. Analysis of patients with heavy ( > 40) versus light ( < 10) pack-year burden suggested an augmented dedifferentiation profile in heavy smokers. In this example, improved classification was observed through features compression through directed network scoring compared to using individual RNA measurements selected by filtration methods. Conclusions: In-silico analysis of lung cancer patient biopsies generated hypotheses implicating stem cell signaling in tumors, and a further stratification of this signal based on patient pack year burden. Mechanistic modeling may be a useful application to the overfitting problem often encountered with -omics data in translational studies. Data from other TCGA indications can be used to evaluate the consistency of this type of approach
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Yuan, Lin, Le-Hang Guo, Chang-An Yuan, Youhua Zhang, Kyungsook Han, Asoke K. Nandi, Barry Honig, and De-Shuang Huang. "Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 3 (May 1, 2019): 782–91. http://dx.doi.org/10.1109/tcbb.2018.2866836.
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Panchal, Viral, and Daniel F. Linder. "Reverse engineering gene networks using global–local shrinkage rules." Interface Focus 10, no. 1 (December 13, 2019): 20190049. http://dx.doi.org/10.1098/rsfs.2019.0049.
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Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global–local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.
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Chen, Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush, and Camila M. Lopes-Ramos. "Abstract 3490: Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks." Cancer Research 84, no. 6_Supplement (March 22, 2024): 3490. http://dx.doi.org/10.1158/1538-7445.am2024-3490.
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Abstract Sex differences in lung adenocarcinoma (LUAD) are evident in incidence rates, prognostic outcomes, and therapy responses, yet the underlying molecular mechanisms driving these disparities remain underexplored. In this study, we conducted a comprehensive proteogenomic analysis encompassing 38 females and 73 males with LUAD from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Employing Transcription Inference using Gene Expression and Regulatory data (TIGER), we inferred sex-differentially activated transcription factors (TFs) from The Cancer Genome Atlas (TCGA) LUAD gene expression data and identified sex-differentially activated kinases using CPTAC protein phosphorylation data. We further constructed a comprehensive kinase-TF signaling network by integrating these sex-differentially activated kinases with TFs, identifying all paths shorter than 3 in the protein interaction networks to highlight druggable pathways. Our analyses revealed that many proteins exhibit not only sex-biased abundance but also sex-biased phosphorylation and acetylation. Furthermore, these sex-biased proteins were associated with critical biological pathways including cell proliferation, immune response, and metabolism. Using kinase-TF signaling networks, we found substantial sex bias in the activities of clinically actionable TFs and kinases, including the glucocorticoid receptor (NR3C1), AR, AURKA, CDK6, and MAPK14. Leveraging the PRISM cancer cell line screening database, we identified several small-molecule drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors, potentially exhibiting sex-specific efficacy as LUAD therapeutics. Our findings showed that the activity of some clinically relevant TFs and kinases differ by sex in LUAD, underscoring the need to consider sex as a biological variable and the utility of multi-omics integrative protein signaling networks in advancing our understanding of cancer biology and the development of sex-aware therapeutics. Citation Format: Chen Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush, Camila M. Lopes-Ramos. Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3490.
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Wani, Nisar, and Khalid Raza. "MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks." PeerJ Computer Science 7 (January 28, 2021): e363. http://dx.doi.org/10.7717/peerj-cs.363.
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High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.
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Qian, Yichun, and Shao-shan Carol Huang. "Improving plant gene regulatory network inference by integrative analysis of multi-omics and high resolution data sets." Current Opinion in Systems Biology 22 (August 2020): 8–15. http://dx.doi.org/10.1016/j.coisb.2020.07.010.
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Benedetti, Elisa, Nathalie Gerstner, Maja Pučić-Baković, Toma Keser, Karli R. Reiding, L. Renee Ruhaak, Tamara Štambuk, et al. "Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference." Metabolites 10, no. 7 (July 2, 2020): 271. http://dx.doi.org/10.3390/metabo10070271.
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Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography-ElectroSpray Ionization-Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization-Furier Transform Ion Cyclotron Resonance-Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements.
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Conard, Ashley Mae, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, and Erica Larschan. "TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data." Nucleic Acids Research 49, W1 (June 14, 2021): W641—W653. http://dx.doi.org/10.1093/nar/gkab384.
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Abstract Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
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Zeng, Irene Sui Lan, and Thomas Lumley. "Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)." Bioinformatics and Biology Insights 12 (January 1, 2018): 117793221875929. http://dx.doi.org/10.1177/1177932218759292.
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Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
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Neutsch, Steffen, Caroline Heneka, and Marcus Brüggen. "Inferring astrophysics and dark matter properties from 21 cm tomography using deep learning." Monthly Notices of the Royal Astronomical Society 511, no. 3 (January 29, 2022): 3446–62. http://dx.doi.org/10.1093/mnras/stac218.
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ABSTRACT 21 cm tomography opens a window to directly study astrophysics and fundamental physics of early epochs in our Universe’s history, the Epoch of Reionization (EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit information encoded in this signal due to its highly non-Gaussian nature. Here, we adopt a network-based approach for direct inference of CD and EoR astrophysics jointly with fundamental physics from 21 cm tomography. We showcase a warm dark matter (WDM) universe, where dark matter density parameter Ωm and WDM mass mWDM strongly influence both CD and EoR. Reflecting the three-dimensional nature of 21 cm light-cones, we present a new, albeit simple, 3D convolutional neural network (3D-21cmPIE-Net) for efficient parameter recovery at moderate training cost. On simulations we observe high-fidelity parameter recovery for CD and EoR astrophysics (R2 > 0.78–0.99), together with DM density Ωm (R2 > 0.97) and WDM mass (R2 > 0.61, significantly better for $m_\mathrm{WDM}\lt 3\!-\!4\,$ keV). For realistic mock observed light-cones that include noise and foreground levels expected for the Square Kilometre Array, we note that in an optimistic foreground scenario parameter recovery is unaffected, while for moderate, less optimistic foreground levels (occupying the so-called wedge) the recovery of the WDM mass deteriorates, while other parameters remain robust against increased foreground levels at R2 > 0.9. We further test the robustness of our network-based inference against modelling uncertainties and systematics by transfer learning between bare simulations and mock observations; we find robust recovery of specific X-ray luminosity and ionizing efficiency, while DM density and WDM mass come with increased bias and scatter.
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Ultsch, Alfred, and Jörn Lötsch. "Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data." International Journal of Molecular Sciences 23, no. 22 (November 15, 2022): 14081. http://dx.doi.org/10.3390/ijms232214081.
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Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold ε) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p < ε). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.
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Yang, Jiyuan, Sheetal Bhatara, Masayuki Umeda, Shanshan Bradford, SongEun Lim, Tamara Westover, Jing Ma, Lauren Ezzell, Jeffery Klco, and Jiyang Yu. "Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics." Blood 142, Supplement 1 (November 28, 2023): 5977. http://dx.doi.org/10.1182/blood-2023-189178.
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Introduction: Pediatric acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by various chromosomal abnormalities and somatic mutations. Investigating the origin of tumorigenesis and deciphering the complex interplay between AML cells and their surrounding tumor immune microenvironment (TIME) is crucial to understand the underlying mechanisms driving disease progression and response to therapy. Recent advancements in single-cell multi-omics technologies enabled us to characterize the transcriptional (GEX) and epigenetic (ATAC) landscapes of both tumors and TIMEs. Leveraging our in-house algorithm, scMINER (single-cell Mutual Information-based Network Engineering Ranger, Ding et al., 2023), this study aims to build a comprehensive cell type atlas in diverse pediatric AML bone marrow samples and construct intra- and inter-cellular networks, enabling the identification of hidden drivers that shape subtype-specific AML-TIME interactions. Methods: Using CD45 and CD33 as selective markers by flow cytometry, we have successfully isolated blasts and enriched for T cell and B cell populations for 10X single-cell multiome profiling across AML molecular subtypes, including UBTF-TD (n=1), NPM1 (n=1), and KMT2A rearrangement (n=1). We interrogated the GEX data using scMINER, including two key steps: (i) mutual information-based clustering analysis (MICA) and (ii) mutual information-based network inference engine (MINIE). MICA characterizes the intrinsic nonlinear similarity of gene expression distributions among cells, enabling a high resolution of clusters. MINIE reverse-engineers cluster-specific intracellular gene networks and infers protein activity of transcription factors (TF) or signaling factors (SIG) drivers based on the expression of its predicted regulon targets in the corresponding cluster. The activity profiles overcome the dropout effects of single-cell RNA-seq data and reflect protein activities across all cells. ArchR is used to analyze ATAC data. CellChat is used to construct and evaluate intercellular networks. Results: AML cells from three samples were divided into four major clusters, and we labeled them as NPM1, KMT2A, UBTF_one, and UBTF_two based on sample subtypes (Fig.1A). MICA managed to obtain distinct AML cell sub-clusters. Comparison with single-cell profiles of normal hematopoietic cells and annotation with marker genes revealed their correspondence with different myeloid developmental stages. Notably, the KMT2A cluster shared a high similarity in cell type proportion with the UBTF_two cluster, and the NPM1 cluster is closer to the UBTF_one cluster. This result demonstrated significant tumor heterogeneity within and across AML molecular subtypes. With the help of intracellular gene networks generated by MINIE, we identified multiple subtype-specific hidden drivers by differential activity analysis. For example, KMT2A and UBTF_two clusters exhibited elevated SAMHD1 levels, consistent with previous GSE data (Zhang et al., 2022). Chromatin accessibility analysis of ATAC data showed increased peaks near SAMHD1 in KMT2A and UBTF_two clusters, further supporting this observation. IRF8 showed a similar expression pattern to SAMHD1, suggesting its context-dependent role across AML subtypes. Accurate annotation of myeloid and immune cells greatly enriches the preciseness of the AML and TIME interaction patterns, which vary across AML molecular subtypes (Fig.1B). Interestingly, cells originated from KMT2A cluster exhibited similar interaction patterns to UBTF_two, while NPM1 and UBTF_one were grouped. This result indicated that different AML molecular subtypes may partially utilize the same mechanisms of tumor-TIME interaction, resulting in similar tumor phenotypes. Conclusions: Accurate clustering and well-defined annotation of myeloid and immune cells enabled us to build a comprehensive cell type atlas in AML samples. The subtype-specific network and the following analysis identified candidate hidden drivers that possibly contribute to the heterogeneity of tumor populations. AML subtypes sharing the same hidden drivers have similar cell type proportions and interaction patterns with TIMEs. This observation implies that AML subtypes share cell type-specific dependencies, which can be therapeutic targets to overcome the refractoriness posed by tumor heterogeneity.
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Fang, Yan, Jiayin Yu, Yumei Ding, and Xiaohua Lin. "Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning." Mathematics 11, no. 22 (November 20, 2023): 4709. http://dx.doi.org/10.3390/math11224709.
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Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relationship. The methodology employed in our study unfolds through several stages. First, a knowledge graph refining entities and relationships from data was constructed. Second, we developed a two-stage knowledge representation learning method to better represent the structured and unstructured knowledge based on TransE and SBERT. Then, the relationship inferring problem was converted into a path reasoning problem under the Markov decision process environment by learning a dynamic policy network. We also applied a soft pruning strategy and a modified reward function to improve the effectiveness of the policy network training. We demonstrate the effectiveness of the proposed method on standard Amazon datasets, and it gives about 5–15% relative improvement over the state-of-the-art models in terms of NDCG@10, Recall@10, Precision @10, and HR@10.
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Klepikova, Anna V., and Aleksey A. Penin. "Gene Expression Maps in Plants: Current State and Prospects." Plants 8, no. 9 (August 28, 2019): 309. http://dx.doi.org/10.3390/plants8090309.
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For many years, progress in the identification of gene functions has been based on classical genetic approaches. However, considerable recent omics developments have brought to the fore indirect but high-resolution methods of gene function identification such as transcriptomics, proteomics, and metabolomics. A transcriptome map is a powerful source of functional information and the result of the genome-wide expression analysis of a broad sampling of tissues and/or organs from different developmental stages and/or environmental conditions. In plant science, the application of transcriptome maps extends from the inference of gene regulatory networks to evolutionary studies. However, only some of these data have been integrated into databases, thus enabling analyses to be conducted without raw data; without this integration, extensive data preprocessing is required, which limits data usability. In this review, we summarize the state of plant transcriptome maps, analyze the problems associated with the combined analysis of large-scale data from various studies, and outline possible solutions to these problems.
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Chen, Xi, Yuan Wang, Antonio Cappuccio, Wan-Sze Cheng, Frederique Ruf Zamojski, Venugopalan D. Nair, Clare M. Miller, et al. "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data." Nature Computational Science 3, no. 7 (July 25, 2023): 644–57. http://dx.doi.org/10.1038/s43588-023-00476-5.
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AbstractResolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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Guo, Tingbo, Haiqi Zhu, Xiao Wang, Jia Wang, Xinyu Zhou, Yuhui Wei, Pengtao Dang, Chi Zhang, and Sha Cao. "Abstract 2072: Computational modeling of metabolic variations in tumor microenvironment." Cancer Research 83, no. 7_Supplement (April 4, 2023): 2072. http://dx.doi.org/10.1158/1538-7445.am2023-2072.
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Abstract Dysregulation of metabolic pathways is a hallmark of cancer. Despite a plethora of knowledge on the core components of metabolic pathways we have gained, there are still major gaps in our understanding of the integrated behavior and metabolic heterogeneity of cells in the context of their microenvironment. Essentially, metabolic behavior can be determined by different factors and vary dramatically from cell to cell due to their high plasticity, driven by the need to cope with various dynamic metabolic requirements. Large amount of single-cell, spatial or tissue multi-omics data obtained from disease tissue has been proven to be endowed with the potential to deliver information on a cell functioning state and its underlying phenotypic switches.We have recently developed single-cell flux estimation analysis (scFEA) to predict sample-wise metabolic fluxome by using single-cell or bulk transcriptomics data. We further developed a web portal scFLUX.org, empowered by our scFEA method, expanding the analytic ability to both human and mouse metabolic networks. We also developed a probabilistic model named Michaelis-Menten-based Flux Estimation Analysis (mmFEA) for statistical inference of flux changes between conditions or cell types based on matched or unmatched gene expression data, metabolomics data, and partially observed kinetic parameters. These methods provide the following unmet capabilities to study metabolic variations in cancer: (1) reconstruction of cell or tissue specific and subcellular-resolution metabolic network, (2) estimation of cell-/sample-wise metabolic flux by considering metabolic imbalance, metabolic exchange between cells, and shifted redox, pH and energy balance in disease microenvironment, (3) a systematic evaluation of functional impact of variations in gene expression, genetic/epigenetic variations, metabolite availability and network structure on the context specific metabolic network and flux, (4) estimate the impact of each gene on flux by using a first-order derivative, (5) perturbation analysis to predict how alterations in enzymes or metabolites may affect the metabolic flux, and (6) identification of the samples and sub-network of a distinct metabolic shift.The methods were validated on matched scRNA-seq, metabolomics and fluxomics data. We demonstrated the function of this computational framework on pan-cancer transcriptomics and proteomics data, and single cell and spatial transcriptomics data. Citation Format: Tingbo Guo, Haiqi Zhu, Xiao Wang, Jia Wang, Xinyu Zhou, Yuhui Wei, Pengtao Dang, Chi Zhang, Sha Cao. Computational modeling of metabolic variations in tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2072.
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Jin, Qiao, and Ronald Ching Wan Ma. "Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies." Cells 10, no. 11 (October 21, 2021): 2832. http://dx.doi.org/10.3390/cells10112832.
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The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.
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Schwaber, Jessica L., Darren Korbie, Stacey Andersen, Erica Lin, Panagiotis K. Chrysanthopoulos, Matt Trau, and Lars K. Nielsen. "Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships." PLOS ONE 16, no. 2 (February 5, 2021): e0246107. http://dx.doi.org/10.1371/journal.pone.0246107.
Повний текст джерелаАнотація:
With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to draw simple regulatory inferences. In response we have applied a multi-omics approach using primary blood progenitor cells primed for homogeneous proliferation and granulocyte differentiation states which combines whole transcriptome resequencing (Ampliseq RNA) supported by droplet digital PCR (ddPCR) validation and mass spectrometry-based proteomics in a hypothesis-generation study of neutrophil differentiation pathways. Primary CD34+ cells isolated from human cord blood were first precultured in non-lineage driving medium to achieve an active, proliferating phenotype from which a neutrophil primed progenitor was isolated and cultured in neutrophil lineage supportive medium. Samples were then taken at 24-hour intervals over 9 days and analysed by Ampliseq RNA and mass spectrometry. The Ampliseq dataset depth, breadth and quality allowed for several unexplored transcriptional regulators and ncRNAs to be identified using a combinatorial approach of hierarchical clustering, enriched transcription factor binding motifs, and network mapping. Network mapping in particular increased comprehension of neutrophil differentiation regulatory relationships by implicating ARNT, NHLH1, PLAG1, and 6 non-coding RNAs associated with PU.1 regulation as cell-engineering targets with the potential to increase total neutrophil culture output. Overall, this study develops and demonstrates an effective new hypothesis generation methodology for transcriptome profiling during differentiation, thereby enabling identification of novel gene targets for editing interventions.
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Majumdar, Abhishek, Yueze Liu, Yaoqin Lu, Shaofeng Wu, and Lijun Cheng. "kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression." Genes 12, no. 6 (May 30, 2021): 844. http://dx.doi.org/10.3390/genes12060844.
Повний текст джерелаАнотація:
Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.
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Clark, Natalie M., Trevor M. Nolan, Ping Wang, Gaoyuan Song, Christian Montes, Conner T. Valentine, Hongqing Guo, Rosangela Sozzani, Yanhai Yin, and Justin W. Walley. "Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis." Nature Communications 12, no. 1 (October 6, 2021). http://dx.doi.org/10.1038/s41467-021-26165-3.
Повний текст джерелаАнотація:
AbstractBrassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use our network predictions to identify putative phosphorylation sites on BES1 and experimentally validate their importance. Additionally, we identify BRONTOSAURUS (BRON) as a transcription factor that regulates cell division, and we show that BRON expression is modulated by BR-responsive kinases and transcription factors. This work demonstrates the power of integrative network analysis applied to multi-omic data and provides fundamental insights into the molecular signaling events occurring during BR response.
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Ben Guebila, Marouen, Tian Wang, Camila M. Lopes-Ramos, Viola Fanfani, Des Weighill, Rebekka Burkholz, Daniel Schlauch, et al. "The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks." Genome Biology 24, no. 1 (March 9, 2023). http://dx.doi.org/10.1186/s13059-023-02877-1.
Повний текст джерелаАнотація:
AbstractInference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.
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Kim, Daniel, Andy Tran, Hani Jieun Kim, Yingxin Lin, Jean Yee Hwa Yang, and Pengyi Yang. "Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data." npj Systems Biology and Applications 9, no. 1 (October 19, 2023). http://dx.doi.org/10.1038/s41540-023-00312-6.
Повний текст джерелаАнотація:
AbstractInferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Fotuhi Siahpirani, Alireza, Sara Knaack, Deborah Chasman, Morten Seirup, Rupa Sridharan, Ron Stewart, James Thomson, and Sushmita Roy. "Dynamic regulatory module networks for inference of cell type-specific transcriptional networks." Genome Research, June 15, 2022, gr.276542.121. http://dx.doi.org/10.1101/gr.276542.121.
Повний текст джерелаАнотація:
Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic datasets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present Dynamic Regulatory Module Networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage or time point, and uses multi-task learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each exhibiting different temporal relationships and measuring a different combination of regulatory genomic datasets: cellular reprogramming, liver dedifferentiation and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns demonstrating its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic datasets.
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Ogris, Christoph, Yue Hu, Janine Arloth, and Nikola S. Müller. "Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data." Scientific Reports 11, no. 1 (March 24, 2021). http://dx.doi.org/10.1038/s41598-021-85544-4.
Повний текст джерелаАнотація:
AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.
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Capobianco, Enrico, Elisabetta Marras, and Antonella Travaglione. "Multiscale Characterization of Signaling Network Dynamics through Features." Statistical Applications in Genetics and Molecular Biology 10, no. 1 (January 20, 2011). http://dx.doi.org/10.2202/1544-6115.1657.
Повний текст джерелаАнотація:
Inference methods applied to biological networks suffer from a main criticism: as the latter reflect associations measured under static conditions, an evaluation of the underlying modular organization can be biologically meaningful only if the dynamics can also be taken into consideration. The same limitation is present in protein interactome networks. Given the substantial uncertainty characterizing protein interactions, we identify at least three aspects that must be considered for inference purposes: 1. Coverage, which for most organisms is only partial; 2. Stochasticity, affecting both the high-throughput experimental and the computational settings from which the interactions are determined, and leading to suboptimal measurement accuracy; 3. Information variety, due to the heterogeneity of technological and biological sources generating the data. Consequently, advances in inference methods require adequate treatment of both system uncertainty and dynamical aspects. Feasible solutions are often made possible by data (omic) integration procedures that complement the experimental design and the computational approaches for network modeling. We present a multiscale stochastic approach to deal with protein interactions involved in a well-known signaling network, and show that based on some topological network features, it is possible to identify timescales (or resolutions) that characterize complex pathways.
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Zhang, Shilu, Saptarshi Pyne, Stefan Pietrzak, Spencer Halberg, Sunnie Grace McCalla, Alireza Fotuhi Siahpirani, Rupa Sridharan, and Sushmita Roy. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets." Nature Communications 14, no. 1 (May 27, 2023). http://dx.doi.org/10.1038/s41467-023-38637-9.
Повний текст джерелаАнотація:
AbstractCell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.