Relevant bibliographies by topics / Multi-omic

Academic literature on the topic 'Multi-omic'

Author: Grafiati
Published: 9 March 2023
Last updated: 14 March 2023

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Journal articles on the topic "Multi-omic"

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Rappoport, Nimrod, Roy Safra, and Ron Shamir. "MONET: Multi-omic module discovery by omic selection." PLOS Computational Biology 16, no. 9 (September 15, 2020): e1008182. http://dx.doi.org/10.1371/journal.pcbi.1008182.

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Morota, Gota. "30 Mutli-omic data integration in quantitative genetics." Journal of Animal Science 97, Supplement_2 (July 2019): 15. http://dx.doi.org/10.1093/jas/skz122.027.

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Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.
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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (November 27, 2020): 1606. http://dx.doi.org/10.3390/biom10121606.

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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.
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Li, Jin, Feng Chen, Hong Liang, and Jingwen Yan. "MoNET: an R package for multi-omic network analysis." Bioinformatics 38, no. 4 (October 25, 2021): 1165–67. http://dx.doi.org/10.1093/bioinformatics/btab722.

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Abstract Motivation The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism. Results Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation. Availability and implementation The MoNET package is freely available on https://github.com/JW-Yan/MONET. Supplementary information Supplementary data are available at Bioinformatics online.
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Chu, Su, Mengna Huang, Rachel Kelly, Elisa Benedetti, Jalal Siddiqui, Oana Zeleznik, Alexandre Pereira, et al. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective." Metabolites 9, no. 6 (June 18, 2019): 117. http://dx.doi.org/10.3390/metabo9060117.

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It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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Demirel, Habibe Cansu, Muslum Kaan Arici, and Nurcan Tuncbag. "Computational approaches leveraging integrated connections of multi-omic data toward clinical applications." Molecular Omics 18, no. 1 (2022): 7–18. http://dx.doi.org/10.1039/d1mo00158b.

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Data integration approaches are crucial for transforming multi-omic data sets into clinically interpretable knowledge. This review presents a detailed and extensive guideline to catalog the recent computational multi-omic data integration methods.
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Boekel, Jorrit, John M. Chilton, Ira R. Cooke, Peter L. Horvatovich, Pratik D. Jagtap, Lukas Käll, Janne Lehtiö, Pieter Lukasse, Perry D. Moerland, and Timothy J. Griffin. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (February 2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.

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Daliri, Eric Banan-Mwine, Fred Kwame Ofosu, Ramachandran Chelliah, Byong H. Lee, and Deog-Hwan Oh. "Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies." Biomolecules 11, no. 2 (February 17, 2021): 300. http://dx.doi.org/10.3390/biom11020300.

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The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host–microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.
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Shaba, Enxhi, Lorenza Vantaggiato, Laura Governini, Alesandro Haxhiu, Guido Sebastiani, Daniela Fignani, Giuseppina Emanuela Grieco, Laura Bergantini, Luca Bini, and Claudia Landi. "Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone." Proteomes 10, no. 2 (April 22, 2022): 12. http://dx.doi.org/10.3390/proteomes10020012.

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In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.
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Le Bras, Alexandra. "A multi-omic resource of mouse neutrophils." Lab Animal 50, no. 9 (August 25, 2021): 239. http://dx.doi.org/10.1038/s41684-021-00840-w.

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Dissertations / Theses on the topic "Multi-omic"

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Bilbrey, Emma A. "Seeding Multi-omic Improvement of Apple." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594907111820227.

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Xiao, Hui. "Network-based approaches for multi-omic data integration." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289716.

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The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.
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Martínez, Enguita David. "Identification of personalized multi-omic disease modules in asthma." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15987.

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Asthma is a respiratory syndrome associated with airflow limitation, bronchial hyperresponsiveness and inflammation of the airways in the lungs. Despite the ongoing research efforts, the outstanding heterogeneity displayed by the multiple forms in which this condition presents often hampers the attempts to determine and classify the phenotypic and endotypic biological structures at play, even when considering a limited assembly of asthmatic subjects. To increase our understanding of the molecular mechanisms and functional pathways that govern asthma from a systems medicine perspective, a computational workflow focused on the identification of personalized transcriptomic modules from the U-BIOPRED study cohorts, by the use of the novel MODifieR integrated R package, was designed and applied. A feature selection of candidate asthma biomarkers was implemented, accompanied by the detection of differentially expressed genes across sample categories, the production of patient-specific gene modules and the subsequent construction of a set of core disease modules of asthma, which were validated with genomic data and analyzed for pathway and disease enrichment. The results indicate that the approach utilized is able to reveal the presence of components and signaling routes known to be crucially involved in asthma pathogenesis, while simultaneously uncovering candidate genes closely linked to the latter. The present project establishes a valuable pipeline for the module-driven study of asthma and other related conditions, which can provide new potential targets for therapeutic intervention and contribute to the development of individualized treatment strategies.
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DENTI, VANNA. "Development of multi-omic mass spectrometry imaging approaches to assist clinical investigations." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365169.

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Con il termine di –omica spaziale si intende l’insieme di diverse tecniche che consentono di rilevare alterazioni significative delle biomolecole all’interno dei loro tessuti d’origine o delle strutture cellulari, permettendo quindi di integrare ed ampliare la comprensione dei cambiamenti biologici che si verificano in tessuti patologici complessi ed eterogenei, come il cancro. Tuttavia, per comprendere appieno la complessità e le dinamiche al di là delle condizioni patologiche, è necessario studiare e integrare diverse analisi molecolari, come quelle di lipidi e glicani, in modo da ottenere un’istantanea molecolare il più completa ed estesa possibile della malattia. Tra le tecniche di -omica spaziale, quella di desorbimento e ionizzazione laser assistiti da matrice (MALDI) abbinata alla spettrometria di massa imaging (MSI), permette lo studio della componente molecolare del tessuto patologico tramite un approccio multiplex, che permette di esaminare diverse centinaia di biomolecole in una singola analisi. Pertanto, l’analisi MALDI-MSI viene utilizzata per studi -omici spaziali di proteine, peptidi e N-glicani su campioni di tessuti clinici fissati in formalina e inclusi in paraffina (FFPE). Per quanto riguarda i lipidi, invece, questo tipo di analisi è sempre stato considerato poco efficace su campioni FFPE a causa della perdita di una grande quantità di contenuto lipidico durante le fasi di lavaggio con solventi organici, mentre i restanti lipidi resistenti ai solventi sono inaccessibili poiché trattenuti nei legami incrociati della formalina. In questi tre anni di dottorato, abbiamo sviluppato nuovi approcci MALDI-MSI per l'analisi spaziale multi-omica su campioni di tessuto clinico FFPE. Le prime tre pubblicazioni riportate in questa tesi si sono concentrate sullo sviluppo di protocolli MALDI-MSI per lipidi in campioni FFPE. In particolare, due di essi descrivono il metodo di preparazione del campione per la rilevazione di ioni di fosfolipidi carichi positivamente, principalmente fosfatidilcoline (PC), in campioni clinici di carcinoma renale a cellule chiare (ccRCC) e in un modello di xenotrapianto di cancro al seno. La terza pubblicazione riporta la possibilità di utilizzare ioni di fosfolipidi carichi negativamente, principalmente fosfatidilinositoli (PI), per definire firme lipidiche in grado di distinguere i gradi di tumore del colon-retto che presentano diverse quantità di linfociti infiltranti il tumore (TIL). Il lavoro finale propone un originale metodo MALDI-MSI multi-omico per l'analisi sequenziale di lipidi, N-glicani e peptidi triptici su una singola sezione FFPE. In particolare, il metodo è stato inizialmente implementato su replicati tecnici di cervello murino e successivamente utilizzato su campioni di ccRCC, come ulteriore prova, ottenendo una caratterizzazione più completa del tessuto tumorale grazie alla combinazione delle informazioni molecolari. Complessivamente, questi risultati aprono la strada a un nuovo approccio multi-omico spaziale basato sulla spettrometria di massa imaging (MSI) che è in grado di restituire un ritratto molecolare più ampio e più preciso della malattia.
The field of spatial omics defines the gathering of different techniques that allow the detection of significant alterations of biomolecules in the context of their native tissue or cellular structures. As such, they extend the landscape of biological changes occurring in complex and heterogeneous pathological tissues, such as cancer. However, additional molecular levels, such as lipids and glycans, must be studied to define a more comprehensive molecular snapshot of disease and fully understand the complexity and dynamics beyond pathological condition. Among the spatial-omics techniques, matrix-assisted laser desorption/ionisation (MALDI)-mass spectrometry imaging (MSI) offers a powerful insight into the chemical biology of pathological tissues in a multiplexed approach where several hundreds of biomolecules can be examined within a single experiment. Thus, MALDI-MSI has been readily employed for spatial omics studies of proteins, peptides and N-Glycans on clinical formalin-fixed paraffin-embedded (FFPE) tissue samples. Conversely, MALDI-MSI analysis of lipids has always been considered not feasible on FFPE samples due to the loss of a great amount of lipid content during washing steps with organic solvents, with the remaining solvent-resistant lipids being involved in the formalin cross-links. In this three-year thesis work, novel MALDI-MSI approaches for spatial multi-omics analysis on clinical FFPE tissue samples were developed. The first three publications reported in this thesis focused on the development of protocols for MALDI-MSI of lipids in FFPE samples. In particular, two of them describe a sample preparation method for the detection of positively charged phospholipids ions, mainly phosphatidylcholines (PCs), in clinical clear cell Renal Cell Carcinoma (ccRCC) samples and in a xenograft model of breast cancer. The third publication reports the possibility to use negatively charged phospholipids ions, mainly phosphatidylinositols (PIs), to define lipid signatures able to distinguish colorectal cancers with different amount of tumour infiltrating lymphocytes (TILs). The final work proposes a unique multi-omic MALDI-MSI method for the sequential analysis of lipids, N-Glycans and tryptic peptides on a single FFPE section. Specifically, the method feasibility was first established on murine brain technical replicates. The method was consequently used on ccRCC samples, as a proof of concept, assessing a more comprehensive characterisation of the tumour tissue when combining the multi-level molecular information. Altogether, these findings pave the way for new MSI-based spatial multi-omics approach aiming at an extensive and more precise molecular portrait of disease.
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Elsheikh, Samar Salah Mohamedahmed. "Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases." Doctoral thesis, Faculty of Health Sciences, 2020. http://hdl.handle.net/11427/32609.

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Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomics. Studies in these fields have taken different designs and directions from genomewide associations to studying the complex interplay between genetics and structural connectivity of a wide range of brain-related diseases. Nevertheless, such combinations of heterogeneous, high dimensional and inter-related data has introduced new challenges which cannot be handled with traditional statistical methods. In this thesis, we proposed analysis pipelines and methodologies to study the causal relationship between neuroimaging features, including tumour characteristics and connectomics, genetics and clinical factors in brain-related diseases. In doing so, we adopted two longitudinal study designs and modelled the association between Alzheimer's disease progression and genetic factors, utilising local and global brain connectivity networks. In addition to that, we performed a multi-stage radiogenomic analysis in glioblastoma using non-parametric statistical methods. To address some limitations in the methods, we adopted the Structural Equation Model and developed a mathematical model to examine the inter-correlation between neuroimaging and multi-omic characteristics of brain-related diseases. Our findings have successfully identified risk genes that were previously reported in the literature of Alzheimer's and glioblastoma diseases, and discovered potential risk variants which associate with disease progression. More specifically, we found some loci in the genes CDH18, ANTXR2 and IGF1, located in Chromosomes 5, 4 and 12, to have effect on the brain connectivity over time in Alzheimer's disease. We also found that the expression of APP, HFE, PLAU and BLMH have significant effects on the structural connectivity of local areas in the brain, these are the left Heschl gyrus, right anterior cingulate gyrus, left fusiform gyrus and left Heschl gyrus, respectively. These potential association patterns could be useful for early disease diagnosis, treatment and neurodegeneration prediction. More importantly, we identified gaps in the imaging genetics methodologies, we proposed a mathematical model accounting for these limitations and evaluated the model which produced promising results. Our proposed flexible model, BiGen, addresses the gaps in the existing tools by combining neuroimaging, genetics, environmental, and phenotype information to a single complex analysis, accounting for the heterogeneity, inter-correlation, and non-linearity of the variables. Moreover, BiGen adopts an important assumption which is hardly met in the literature of imaging genetics, and that is, all the four variables are assumed to be latent constructs, that means they can not be observed directly from the data, and are measured through observed indicators. This is an important assumption in both neuroimaging, behavioural and genetic studies, and it is one of the reasons why BiGen is flexible and can easily be extended to include more indicators and latent constructs in the context of brain-related diseases.
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Ciaccio, Roberto <1990&gt. "Multi-omic analyses of the MYCN network unveil new potential vulnerabilities in childhood neuroblastoma." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9930/1/PhD%20thesis%20Ciaccio%20Roberto_2021.pdf.

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Neuroblastoma is the first neurogenic-extracranial solid cancer occurring in infancy and childhood. The genetic aberration most commonly associated with a poor prognosis is MYCN gene’s amplification. We hypothesize that effective anti-MYC therapeutics can be developed by understanding the regulation and function of N-MYC in neuroblastoma. Since N-MYC is an intrinsically disordered protein, it is still challenging to target this transcription factor, however, the model is shifting significantly after discovering novel therapeutic targets that impact MYC-driven tumorigenesis. The following work explores how MYCN expression affects the induction and maintenance of neuroblastoma. By using different multi-omic approaches and many promising innovative techniques, we were able to identify and characterize new potential vulnerabilities of this pathology, which may work in concert with N-MYC for the instruction of a high-risk neuroblastoma phenotype. My studies’ first objective was to investigate whether and how N-MYC can regulate transcription of lncRNAs by comparing transcriptional profiles between non-amplified and MYCN-amplified neuroblastoma cells. Here, we singled out lncNB1, which is selectively higher expressed in high MYCN cells only and it is also firmly and almost uniquely transcribed in neuroblastoma among all types of cancers. Our data showed that N-MYC directly activates transcription of lncNB1, instructing a complex network of molecular interactions, ultimately resulting in increased N-MYC protein stability, reinforcing the N-MYC oncogenetic program. The second objective was to assess how high N-MYC expression may cooperate to establish a dynamic regulatory axis with the E2F3 transcription factor, impacting the development of the high-risk cancer phenotype. Taken together, our unbias screenings uncovered potential candidates that help to fill the knowledge gap in understanding what is the impact of N-MYC in childhood neuroblastoma, providing new opportunities for the development of specific treatments able to target the function of MYC oncoproteins in a context of MYCN gene amplification.
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Lingam, Shivanjali. "Multi-Omic Characterisation of the Kidney in a Rodent Model of Type Two Diabetes Mellitus." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23717.

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Type 2 diabetes mellitus (T2DM) is the most rapidly growing disease worldwide, with a more than four-fold increase in diagnosed people in the last 30 years. Almost 500 million people are affected, and many more are thought to be undiagnosed. T2DM is a disorder of human metabolism resulting from the resistance of peripheral tissues to the hormone insulin, which is produced by the β-cells of the pancreas. Insulin regulates blood glucose levels and therefore insulin resistance can generate a profound hyperglycemia that itself has major health risks. T2DM is largely considered therefore to be a ‘lifestyle’ disease, involving energetic excess from poor diet and physical inactivity. T2DM is a major risk factor for several comorbidities including cardiovascular disease (atherosclerosis and stroke), non-alcoholic fatty liver disease (NAFLD) and ocular and neurological disorders (retinopathy and neuropathy). Another major potential consequence of T2DM is renal disease known as ‘diabetic nephropathy’ (DN), which is a microvascular complication eventually leading to end-stage renal disease (ESRD). Despite this, the vast majority of molecular studies aimed at better understanding the basis of pathogenesis in T2DM have examined cell lines or other tissues (e.g. the liver). Given that the first major symptoms of T2DM include polydipsia (increased thirst) and polyuria (increased urination), which both have some basis in the regulation of blood pressure in the kidney glomerulus, a complete analysis of the T2DM kidney is somewhat overdue. Human studies of T2DM are plagued with reproducibility issues due to person-person differences, including age, diet, current drug treatment and genetics. We therefore employed a reproducible rodent (rat) model of T2DM that utilises a combination of high fat (HF) diet and low-dose streptozotocin (STZ) injection, which induces pancreatic insufficiency via β-cell dysfunction. Animals subjected to a single treatment (HF diet or STZ injection) or to neither were used as controls. Biochemical and physiological testing showed the T2DM animals showed all traits of human disease, including weight gain, elevated blood glucose levels and reduced insulin tolerance. Histological and EchoMRI analysis of the T2DM kidney demonstrated both morphological defects and physiological alterations consistent with human T2DM, including changes to glomerular health and the formation of structures akin to Kimmelstein-Wilson nodules seen in DN. We demonstrated that application of large-scale high-throughput profiling of the proteome permits systematic assessment of proteins from T2DM kidney tissue. Major changes were observed in pathways associated with metabolism, including the tricarboxylic acid (TCA) cycle and fatty acid biosynthesis / metabolism. We also performed a comparative analysis of the urine from these animals, which showed changes to urinary albumin (increased abundance) and major urinary protein Mup1 (decreased abundance) consistent with human disease. Furthermore, we identified 4 proteins, including apolipoprotein A2, alpha amylase and regenerating islet-derived protein 3, which could be putative urinary biomarkers for T2DM-associated DN. Proteome-level analysis highlighted pathways associated with oxidative stress and signal transduction as altered in animals subjected to HF diet and STZ injection (T2DM). Given the known role of phosphorylation-based signalling in transmitting the insulin response and the dysregulation of insulin signalling in T2DM liver, we next examined renal signalling in T2DM animals by phosphoproteomics. Additional experiments examined the role of reactive oxygen species (ROS) in protein post-translational modifications (PTMs) in these animals. More than 20,000 sites of PTM were identified and quantified across the 4 biological groups. While many pathways were broadly influenced by HF diet and β-cell dysfunction, significant alterations in T2DM animals across both the phospho- and redox-proteome were observed in the mitochondrial TCA cycle, pyruvate metabolism and glycolysis / gluconeogenesis pathways. For example, multiple cysteine redox PTMs were observed as significantly regulated in T2DM-like animals on phosphoenolpyruvate carboxylase (PEPCK), which lies at the nexus of those three pathways, and confirms a role for ROS in mediating cross-talk between metabolic pathways resulting in altered cell signalling and pathway flux. We further speculate that the identified pathways may be linked to structural changes in glomerular podocytes, as alterations in these pathways correlate with previously reported abnormalities in podocyte biology and are characteristic of DN. This thesis has provided a comprehensive molecular multi-omic analysis of the kidney in a rodent model of T2DM. Future studies will be needed to further validate our data, using metabolomics and lipidomics style approaches, coupled with functional studies to determine the role of protein and protein PTM changes in enzyme catalysis and pathway flux. Furthermore, translation of these results into the clinic will require testing of large human cohorts, for example to determine the efficacy of putative urinary markers identified here. By further characterising the roles of proteins and their PTMs, altered protein interactions and related pathways, and how they go on to form an integrated network, we have endeavoured to better understand the molecular mechanisms underlying T2DM-induced DN. Given the complexity and multi-organ involvement in T2DM, better understanding the renal proteome provides a useful resource in enabling stratification of DN diagnosis and improved options for interventional therapies.
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Angione, Claudio. "Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/252943.

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To paraphrase Stan Ulam, a Polish mathematician who became a leading figure in the Manhattan Project, in this dissertation I focus not only on how computer science can help biologists, but also on how biology can inspire computer scientists. On one hand, computer science provides powerful abstraction tools for metabolic networks. Cell metabolism is the set of chemical reactions taking place in a cell, with the aim of maintaining the living state of the cell. Due to the intrinsic complexity of metabolic networks, predicting the phenotypic traits resulting from a given genotype and metabolic structure is a challenging task. To this end, mathematical models of metabolic networks, called genome-scale metabolic models, contain all known metabolic reactions in an organism and can be analyzed with computational methods. In this dissertation, I propose a set of methods to investigate models of metabolic networks. These include multi-objective optimization, sensitivity, robustness and identifiability analysis, and are applied to a set of genome-scale models. Then, I augment the framework to predict metabolic adaptation to a changing environment. The adaptation of a microorganism to new environmental conditions involves shifts in its biochemical network and in the gene expression level. However, gene expression profiles do not provide a comprehensive understanding of the cellular behavior. Examples are the cases in which similar profiles may cause different phenotypic outcomes, while different profiles may give rise to similar behaviors. In fact, my idea is to study the metabolic response to diverse environmental conditions by predicting and analyzing changes in the internal molecular environment and in the underlying multi-omic networks. I also adapt statistical and mathematical methods (including principal component analysis and hypervolume) to evaluate short term metabolic evolution and perform comparative analysis of metabolic conditions. On the other hand, my vision is that a biomolecular system can be cast as a ?biological computer?, therefore providing insights into computational processes. I therefore study how computation can be performed in a biological system by proposing a map between a biological organism and the von Neumann architecture, where metabolism executes reactions mapped to instructions of a Turing machine. A Boolean string represents the genetic knockout strategy and also the executable program stored in the ?memory? of the organism. I use this framework to investigate scenarios of communication among cells, gene duplication, and lateral gene transfer. Remarkably, this mapping allows estimating the computational capability of an organism, taking into account also transmission events and communication outcomes.
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Thavamani, Abhishek [Verfasser], and Alfred [Akademischer Betreuer] Nordheim. "Integrated multi-omic analysis of HCC formation in the SRF-VP16iHep mouse model / Abhishek Thavamani ; Betreuer: Alfred Nordheim." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1173699864/34.

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Wang, Dongxue [Verfasser], Bernhard [Akademischer Betreuer] Küster, Bernhard [Gutachter] Küster, and Julien [Gutachter] Gagneur. "Comprehensive characterization of the human proteome by multi-omic analyses / Dongxue Wang ; Gutachter: Bernhard Küster, Julien Gagneur ; Betreuer: Bernhard Küster." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1172415145/34.

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Books on the topic "Multi-omic"

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A Multi-omic Precision Oncology Pipeline to Elucidate Mechanistic Determinants of Cancer. [New York, N.Y.?]: [publisher not identified], 2021.

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Tieri, Paolo, Christine Nardini, and Jennifer Elizabeth Dent, eds. Multi-omic Data Integration. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-648-7.

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Romualdi, Chiara, Enrica Calura, Davide Risso, Sampsa Hautaniemi, and Francesca Finotello, eds. Multi-omic Data Integration in Oncology. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-151-0.

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Macha, Muzafar A., Tariq A. masoodi, and Ajaz A. bhat. Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-Omic. Elsevier Science & Technology Books, 2024.

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Book chapters on the topic "Multi-omic"

1

Mason, Christopher E., Sandra G. Porter, and Todd M. Smith. "Characterizing Multi-omic Data in Systems Biology." In Systems Analysis of Human Multigene Disorders, 15–38. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8778-4_2.

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Zou, Yan. "Analyzing Multi-Omic Data with Integrative Platforms." In Integrative Bioinformatics, 377–86. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6795-4_18.

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Parmar, Vandan, and Pietro Lió. "Multi-omic Network Regression: Methodology, Tool and Case Study." In Studies in Computational Intelligence, 611–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05414-4_49.

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Ghosh, Shubhrima, Rameshwar Tiwari, R. Hemamalini, and S. K. Khare. "Multi-omic Approaches for Mapping Interactions Among Marine Microbiomes." In Understanding Host-Microbiome Interactions - An Omics Approach, 353–68. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5050-3_20.

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Barbiero, Pietro, Marta Lovino, Mattia Siviero, Gabriele Ciravegna, Vincenzo Randazzo, Elisa Ficarra, and Giansalvo Cirrincione. "Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network." In Intelligent Computing Theories and Application, 172–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60799-9_15.

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Singla, Diksha, and Manjeet Kaur Sangha. "Multi-omic Approaches to Improve Cancer Diagnosis, Prognosis, and Therapeutics." In Computational Intelligence in Oncology, 411–33. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9221-5_23.

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Tikunov, Andrey P., Jeremiah D. Tipton, Timothy J. Garrett, Sachi V. Shinde, Hong Jin Kim, David A. Gerber, Laura E. Herring, Lee M. Graves, and Jeffrey M. Macdonald. "Green Chemistry Preservation and Extraction of Biospecimens for Multi-omic Analyses." In Methods in Molecular Biology, 267–98. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1811-0_17.

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Li, Chen, Maria Virgilio, Kathleen L. Collins, and Joshua D. Welch. "Single-Cell Multi-omic Velocity Infers Dynamic and Decoupled Gene Regulation." In Lecture Notes in Computer Science, 297–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_18.

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Piening, Brian D., Alexa K. Dowdell, and Michael P. Snyder. "Elucidating Diversity in Obesity-Related Phenotypes Using Longitudinal and Multi-omic Approaches." In Natural Products in Obesity and Diabetes, 63–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92196-5_2.

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Yaneske, Elisabeth, and Claudio Angione. "A Data- and Model-Driven Analysis Reveals the Multi-omic Landscape of Ageing." In Bioinformatics and Biomedical Engineering, 145–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_12.

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Conference papers on the topic "Multi-omic"

1

Bardozzo, Francesco, Pietro Lio, and Roberto Tagliaferri. "Multi omic oscillations in bacterial pathways." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280853.

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Keir, Holly Rachael, Amelia Shoemark, Megan Crichton, Alison Dicker, Jennifer Pollock, Ashley Giam, Andrew Cassidy, et al. "Endotyping bronchiectasis through multi-omic profiling." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.4101.

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Otero-Núñez, Pablo, Christopher Rhodes, John Wharton, Emilia Swietlik, Sokratis Kariotis, Lars Harbaum, Mark Dunning, et al. "Multi-omic profiling in pulmonary arterial hypertension." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.4458.

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Fan, Ziling, Yuan Zhou, and Habtom W. Ressom. "MOTA: Multi-omic integrative analysis for biomarker discovery." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857049.

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Resson, Habtom W. "Multi-omic approaches for liver cancer biomarker discovery." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822481.

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Zuo, Yiming, Guoqiang Yu, Chi Zhang, and Habtom W. Ressom. "A new approach for multi-omic data integration." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999157.

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Ressom, Habtom W., Cristina Di Poto, Alessia Ferrarini, Yunli Hu, Mohammad R. Nezami Ranjbar, Ehwang Song, Rency S. Varghese, et al. "Multi-omic approaches for characterization of hepatocellular carcinoma." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591467.

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Kaczmarek, Emily, Amoon Jamzad, Tashifa Imtiaz, Jina Nanayakkara, Neil Renwick, and Parvin Mousavi. "Multi-Omic Graph Transformers for Cancer Classification and Interpretation." In Pacific Symposium on Biocomputing 2022. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/9789811250477_0034.

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Alves, Sarah Hannah, Cristovao Antunes de Lanna, Karla Tereza Figueiredo Leite, Mariana Boroni, and Marley Maria Bernardes Rebuzzi Vellasco. "Multi-omic data integration applied to molecular tumor classification." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669609.

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Konigsberg, I. R., N. W. Lin, S. Y. Liao, C. Liu, K. MacPhail, M. M. Mroz, E. J. Davidson, L. Li, L. A. Maier, and I. V. Yang. "Multi-Omic Signatures of Sarcoidosis in Bronchoalveolar Lavage Cells." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a4979.

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Reports on the topic "Multi-omic"

1

Banfield, Jill. Multi-‘omic’ analyses of the dynamics, mechanisms, and pathways for carbon turnover in grassland soil under two climate regimes. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1504276.

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