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Relevant bibliographies by topics / Omics data analysi

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Academic literature on the topic 'Omics data analysi'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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Journal articles on the topic "Omics data analysi"

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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.

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Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data impu

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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 (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 work

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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, et al. "Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study." Journal of Clinical Oncology 38, no. 15_suppl (2020): e16743-e16743. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16743.

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e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automate

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Madrid-Márquez, Laura, Cristina Rubio-Escudero, Beatriz Pontes, Antonio González-Pérez, José C. Riquelme, and Maria E. Sáez. "MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation." Applied Sciences 12, no. 8 (2022): 3987. http://dx.doi.org/10.3390/app12083987.

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Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the integration of different layers of information. Methods: We have developed a new software tool, MOMIC, that guides the user through the application of different analysis on a wide range of omic data, from the independent single-omics analysis to the combination of heterogeneous data at different molecular levels. Results: The proposed pipeline is developed as a collection of Jupyter

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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.

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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects tha

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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, mi

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Chauvel, Cécile, Alexei Novoloaca, Pierre Veyre, Frédéric Reynier, and Jérémie Becker. "Evaluation of integrative clustering methods for the analysis of multi-omics data." Briefings in Bioinformatics 21, no. 2 (2019): 541–52. http://dx.doi.org/10.1093/bib/bbz015.

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Abstract Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sens

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Alizadeh, Madeline, Natalia Sampaio Moura, Alyssa Schledwitz, Seema A. Patil, Jacques Ravel, and Jean-Pierre Raufman. "Big Data in Gastroenterology Research." International Journal of Molecular Sciences 24, no. 3 (2023): 2458. http://dx.doi.org/10.3390/ijms24032458.

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Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexu

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Misra, Biswapriya B., Carl Langefeld, Michael Olivier, and Laura A. Cox. "Integrated omics: tools, advances and future approaches." Journal of Molecular Endocrinology 62, no. 1 (2019): R21—R45. http://dx.doi.org/10.1530/jme-18-0055.

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just ‘omics’, the challenges include differences in data cleaning,

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Pan, Jianqiao, Baoshan Ma, Xiaoyu Hou, et al. "The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12353–70. http://dx.doi.org/10.3934/mbe.2022576.

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<abstract> <sec><title>Background</title><p>Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.</p> </sec> <sec>&lt

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Dissertations / Theses on the topic "Omics data analysi"

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MASPERO, DAVIDE. "Computational strategies to dissect the heterogeneity of multicellular systems via multiscale modelling and omics data analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/368331.

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L'eterogeneità pervade i sistemi biologici e si manifesta in differenze strutturali e funzionali osservate sia tra diversi individui di uno stesso gruppo (es. organismi o patologie), sia fra gli elementi costituenti di un singolo individuo (es. cellule). Lo studio dell’eterogeneità dei sistemi biologici e, in particolare, di quelli multicellulari è fondamentale per la comprensione meccanicistica di fenomeni fisiologici e patologici complessi (es. il cancro), così come per la definizione di strategie prognostiche, diagnostiche e terapeutiche efficaci. Questo lavoro è focalizzato sullo svilupp

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Wang, Zhi. "Module-Based Analysis for "Omics" Data." Thesis, North Carolina State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3690212.

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<p> This thesis focuses on methodologies and applications of module-based analysis (MBA) in omics studies to investigate the relationships of phenotypes and biomarkers, e.g., SNPs, genes, and metabolites. As an alternative to traditional single–biomarker approaches, MBA may increase the detectability and reproducibility of results because biomarkers tend to have moderate individual effects but significant aggregate effect; it may improve the interpretability of findings and facilitate the construction of follow-up biological hypotheses because MBA assesses biomarker effects in a function

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Zheng, Ning. "Mediation modeling and analysis forhigh-throughput omics data." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256318.

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There is a strong need for powerful unified statistical methods for discovering underlying genetic architecture of complex traits with the assistance of omics information. In this paper, two methods aiming to detect novel association between the human genome and complex traits using intermediate omics data are developed based on statistical mediation modeling. We demonstrate theoretically that given proper mediators, the proposed statistical mediation models have better power than genome-wide association studies (GWAS) to detect associations missed in standard GWAS that ignore the mediators. F

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Campanella, Gianluca. "Statistical analysis of '-omics' data : developments and applications." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32109.

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In recent years, increasingly efficient molecular biology techniques created new opportunities to harness large-scale repositories of biological material collected in epidemiological studies; however, methods to manipulate and analyse the wealth of information thus generated have lagged behind. The introductory chapter of this thesis presents the multifaceted field of 'computational epidemiology' from the perspectives of molecular biology, measurement theory, and statistical modelling. Focusing on measurement of DNA methylation levels, the author also reviews the state of the art, proposes nov

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Budimir, Iva <1992&gt. "Stochastic Modeling and Correlation Analysis of Omics Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9792/1/Budimir_Iva_tesi.pdf.

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We studied the properties of three different types of omics data: protein domains in bacteria, gene length in metazoan genomes and methylation in humans. Gene elongation and protein domain diversification are some of the most important mechanisms in the evolution of functional complexity. For this reason, the investigation of the dynamic processes that led to their current configuration can highlight the important aspects of genome and proteome evolution and consequently of the evolution of living organisms. The potential of methylation to regulate the expression of genes is usually attributed

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Kim, Jieun. "Computational tools for the integrative analysis of muti-omics data to decipher trans-omics networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28524.

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Regulatory networks define the phenotype, morphology, and function of cells. These networks are built from the basic building blocks of the cell—DNA, RNA, and proteins—and cut across the respective omics layers—genome, transcriptome, and proteome. The resulting omics networks depict a near infinite possibility of nodes and edges that intricately connect the ‘omes’. With the rapid advancement in the technologies that generate omics data in bulk samples and now at single-cell resolution, the field of life sciences is now met with the challenge to connect these omes to generate trans-omics networ

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Ding, Hao. "Visualization and Integrative analysis of cancer multi-omics data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1467843712.

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Castleberry, Alissa. "Integrated Analysis of Multi-Omics Data Using Sparse Canonical Correlation Analysis." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu15544898045976.

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Tellaroli, Paola. "Three topics in omics research." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3423912.

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The rather generic title of this Thesis is due to the fact that several aspects of biological phenomena have been investigated. Most of this work was addressed at the investigation of the limitations of one of the essential tools for analyzing gene expression data: cluster analysis. With several hundred of clustering methods in existence, there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come. In particular, we present a novel algorithm for the clustering of static data and a new strategy for the clustering of

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Ayati, Marzieh. "Algorithms to Integrate Omics Data for Personalized Medicine." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1527679638507616.

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Books on the topic "Omics data analysi"

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Tseng, George C., Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Integrating Omics Data. Cambridge University Press, 2015.

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Tseng, George, Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Big Data in Omics and Imaging: Association Analysis. Taylor & Francis Group, 2017.

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Xiong, Momiao. Big Data in Omics and Imaging: Association Analysis. Taylor & Francis Group, 2017.

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Qingfeng, Chen, Wei Lan, Yi-Ping Phoebe Chen, and Wilson Wen Bin Goh, eds. Graph Embedding Methods for Multiple-Omics Data Analysis. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-600-5.

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Book chapters on the topic "Omics data analysi"

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Ghantasala, Saicharan, Shabarni Gupta, Vimala Ashok Mani, et al. "Omics: Data Processing and Analysis." In Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2837-0_3.

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Österlund, Tobias, Marija Cvijovic, and Erik Kristiansson. "Integrative Analysis of Omics Data." In Systems Biology. Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527696130.ch1.

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Yu, Xiang-Tian, and Tao Zeng. "Integrative Analysis of Omics Big Data." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_7.

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Dunkler, Daniela, Fátima Sánchez-Cabo, and Georg Heinze. "Statistical Analysis Principles for Omics Data." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_5.

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Lü, Jinhu, and Pei Wang. "Data-Driven Statistical Approaches for Omics Data Analysis." In Modeling and Analysis of Bio-molecular Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9144-0_9.

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Han, Maozhen, Na Zhang, Zhangjie Peng, et al. "Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Analysis." In Methodologies of Multi-Omics Data Integration and Data Mining. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_6.

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Chen, Yi-An, Lokesh P. Tripathi, and Kenji Mizuguchi. "Data Warehousing with TargetMine for Omics Data Analysis." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9442-7_3.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.

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AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.

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Zhou, Guangyan, Shuzhao Li, and Jianguo Xia. "Network-Based Approaches for Multi-omics Integration." In Computational Methods and Data Analysis for Metabolomics. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_23.

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Mühlberger, Irmgard, Julia Wilflingseder, Andreas Bernthaler, Raul Fechete, Arno Lukas, and Paul Perco. "Computational Analysis Workflows for Omics Data Interpretation." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_17.

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Conference papers on the topic "Omics data analysi"

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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictio

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Kovatch, Patricia, Anthony Costa, Zachary Giles, Eugene Fluder, Hyung Min Cho, and Svetlana Mazurkova. "Big omics data experience." In SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, 2015. http://dx.doi.org/10.1145/2807591.2807595.

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Klabukov, Il'ya. "ELEMENTS FOR SYSTEMS MEDICINE OF CHOLANGIOPATHIES." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9b506245.44352217.

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The approach to system analysis of bile duct dysfunctions based on analysis of multi-omics data of cholangiocytes is considered. There is suggested that changes in intercellular interactions in tissues of the bile duct cause phenotypic manifestations of the cholangiopathies in the changes in cholangiocyte regulation, which can be evaluated by analysis of changes in the molecular composition of the bile.

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Sunghoon Choi, Soo-yeon Park, Hoejin Kim, Oran Kwon, and Taesung Park. "Analysis for doubly repeated omics data from crossover design." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822782.

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Xing, Wei, Jon Smith, Mike Gavrielides, Steve Hindmarsh, Adam Huffman, and Hai H. Wang. "Nautilus: A Precision-Guided Open Data Architecture for Big Omics Data Analysis." In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2019. http://dx.doi.org/10.1109/icaibd.2019.8836977.

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Ma, Yingning. "Cluster analysis for cancer omics data using Neural Network with data augmentation." In SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning. ACM, 2022. http://dx.doi.org/10.1145/3556384.3556388.

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Jain, Yashita, and Shanshan Ding. "Integrative Sufficient Dimension Reduction Methods for Multi-Omics Data Analysis." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2017. http://dx.doi.org/10.1145/3107411.3108225.

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Sun Kim. "Networks and models for the integrated analysis of multi omics data." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822479.

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Fernandez-Banet, Julio, Anthony Esposito, Scott Coffin, et al. "Abstract 4874: OASIS: A centralized portal for cancer omics data analysis." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-4874.

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Min, Eun Jeong, Changgee Chang, and Qi Long. "Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data." In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2018. http://dx.doi.org/10.1109/dsaa.2018.00021.

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Reports on the topic "Omics data analysi"

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Wrinn, Michael. Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems. Final Technical Report. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1659436.

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Villamizar-Villegas, Mauricio, and Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, 2020. http://dx.doi.org/10.32468/be.1141.

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The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In this paper we decompose the RDD treatment effect into its weighted time-value parts. This analysis adds richness to the RDD estimand, where each time-specific component can be different and informative in a manner that is not expressed by the single cutoff or pooled regressions. To illustrate our methodology, we present two empirical examples: one using repeated cross-sectional data and another using

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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD

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