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Relevant bibliographies by topics / Integrative Omics Analysis

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Academic literature on the topic 'Integrative Omics Analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Integrative Omics Analysis"

1

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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Wu, Cen, Fei Zhou, Jie Ren, Xiaoxi Li, Yu Jiang, and Shuangge Ma. "A Selective Review of Multi-Level Omics Data Integration Using Variable Selection." High-Throughput 8, no. 1 (2019): 4. http://dx.doi.org/10.3390/ht8010004.

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High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying speci

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Duan, Ran, Lin Gao, Yong Gao, et al. "Evaluation and comparison of multi-omics data integration methods for cancer subtyping." PLOS Computational Biology 17, no. 8 (2021): e1009224. http://dx.doi.org/10.1371/journal.pcbi.1009224.

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Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of

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López de Maturana, Evangelina, Lola Alonso, Pablo Alarcón, et al. "Challenges in the Integration of Omics and Non-Omics Data." Genes 10, no. 3 (2019): 238. http://dx.doi.org/10.3390/genes10030238.

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Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the pos

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Mirza, Bilal, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, and Peipei Ping. "Machine Learning and Integrative Analysis of Biomedical Big Data." Genes 10, no. 2 (2019): 87. http://dx.doi.org/10.3390/genes10020087.

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Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Speci

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Thongboonkerd, Visith. "Complex systems analysis by integrative omics." Blood 138, no. 24 (2021): 2448–50. http://dx.doi.org/10.1182/blood.2021012974.

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Brink, Benedikt G., Annica Seidel, Nils Kleinbölting, Tim W. Nattkemper, and Stefan P. Albaum. "Omics Fusion – A Platform for Integrative Analysis of Omics Data." Journal of Integrative Bioinformatics 13, no. 4 (2016): 43–46. http://dx.doi.org/10.1515/jib-2016-296.

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Summary We present Omics Fusion, a new web-based platform for integrative analysis of omics data. Omics Fusion provides a collection of new and established tools and visualization methods to support researchers in exploring omics data, validating results or understanding how to adjust experiments in order to make new discoveries. It is easily extendible and new visualization methods are added continuously. It is available for free under: https://fusion.cebitec.uni-bielefeld.de/

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Wang, Wu, and Ma. "Integrative Analysis of Cancer Omics Data for Prognosis Modeling." Genes 10, no. 8 (2019): 604. http://dx.doi.org/10.3390/genes10080604.

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Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensive

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Et al., Gonesh Chandra Saha. "Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 1384–92. http://dx.doi.org/10.52783/tjjpt.v44.i3.488.

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The advent of high-throughput technologies has ushered in an era of unprecedented data generation in the field of bioinformatics. Omics data, including genomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological systems, but their integration poses significant challenges. Integrative analysis of multi-omics data holds the promise of unraveling complex biological phenomena and enabling personalized medicine. [1] Deep learning, a subset of machine learning, has gained prominence in bioinformatics due to its ability to automatically extract intricate pa

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Sanches, Pedro H. Godoy, Nicolly Clemente de Melo, Andreia M. Porcari, and Lucas Miguel de Carvalho. "Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics." Biology 13, no. 11 (2024): 848. http://dx.doi.org/10.3390/biology13110848.

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With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strate

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Dissertations / Theses on the topic "Integrative Omics Analysis"

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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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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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Toprak, Umut [Verfasser], and Benedikt [Akademischer Betreuer] Brors. "Integrative Analysis of Omics Datasets / Umut Toprak ; Betreuer: Benedikt Brors." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/1201088291/34.

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Kim, Taiyun. "Development of statistical methods for integrative omics analysis in precision medicine." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28838.

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Precision medicine is an integrative approach to the prevention and treatment of complex diseases such as cardiovascular disease that considers an individual’s lifestyle, clinical information, and omics profile. In the last decade, the advances in omics technologies have allowed researchers to gain insight into biological systems and progress to precision medicine. Many omics technology now enables us to rapidly generate, store and analyse data at a large scale. Many efforts have attempted to integrate large-scale multi-batch and multi-omics data. While many strategies have been developed, cha

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Hernández, de Diego Rafael. "Development of bioinformatics resources for the integrative analysis of Next Generation omics data." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/91227.

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The advances in high-throughput sequencing techniques and the technological development accompanying them have favoured the development and popularisation of a new range of genomic research disciplines, collectively known as the omics. These technologies are capable of simultaneously measuring thousands of molecules which are essential for life, including DNA, RNA, proteins, and metabolites. Historically, classical genomic research has followed a reductionist approach by studying the structure, regulation, and function of these biological units independently. However, despite being a powerful

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Bockmayr, Michael [Verfasser]. "Integrative analysis of "omics" data and histopathological features in breast and ovarian cancer / Michael Bockmayr." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2017. http://d-nb.info/1126504262/34.

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Ronen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.

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Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung

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Meng, Chen [Verfasser], Bernhard [Akademischer Betreuer] Küster, and Dmitrij [Akademischer Betreuer] Frischmann. "Application of multivariate methods to the integrative analysis of high-throughput omics data / Chen Meng. Betreuer: Bernhard Küster. Gutachter: Bernhard Küster ; Dmitrij Frischmann." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1082347299/34.

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Abily-Donval, Lénaïg. "Exploration des mécanismes physiopathologiques des mucopolysacharidoses et de la maladie de Fabry par approches "omiques" et modulation de l'autophagie. Urinary metabolic phenotyping of mucopolysaccharidosis type I combining untargeted and targeted strategies with data modeling Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR108.

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Les pathologies lysosomales sont des maladies liées au déficit quantitatif ou qualitatif d’une hydrolase ou d’un transporteur à l’origine d’une atteinte multiviscérale potentiellement sévère. Certaines de ces pathologies sont accessibles à des traitements mais ces thérapeutiques sont uniquement symptomatiques et ne guérissent pas les patients. Même si le phénomène de surcharge peut expliquer entre autres la symptomatologie observée, la physiopathologie de ces maladies est complexe et non précisément connue. Une meilleure connaissance de ces pathologies pourrait permettre d’améliorer leur prise

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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 "Integrative Omics Analysis"

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

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Book chapters on the topic "Integrative Omics Analysis"

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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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Hartmann, Anja, and Anna Maria Jozefowicz. "VANTED: A Tool for Integrative Visualization and Analysis of -Omics Data." In Methods in Molecular Biology. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7411-5_18.

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Velmurugan, Ganesan, and Krishnan Swaminathan. "Integrative Analysis of Host–Microbiota-Based Multi-Omics Data: A Novel Path toward Precision Medicine." In Industry 4.0 Technologies for Education. Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003318378-31.

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Shaulov, Yana, and Serge Ankri. "Integrative Omics Analysis of the Effect of Bacteria on the Resistance of Entamoeba histolytica to Oxidative Stress." In Eukaryome Impact on Human Intestine Homeostasis and Mucosal Immunology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44826-4_3.

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Pooloo, Nabeelah, Anisah Wahed Ghoorah, Zahra Mungloo-Dilmohamud, and Shakuntala Baichoo. "Drug Target Identification Using Integrative Multi-omics Data Analysis and Deep Learning - A Breast Cancer Case Study." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88039-1_27.

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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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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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Gedela, Srinubabu. "Integration, Warehousing, and Analysis Strategies of Omics Data." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_18.

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Lim, Hansaim, and Lei Xie. "Omics Data Integration and Analysis for Systems Pharmacology." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9089-4_11.

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Conference papers on the topic "Integrative Omics Analysis"

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Li, Xiaoli, Rui Zhang, Saba Aslam, et al. "scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822866.

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Nikshya, J. Ebens, M. Saravana Karthikeyan, Shalini Prasad, R. Santhana Krishnan, S. Balamurugan, and J. Relin Francis Raj. "A Machine Learning Framework for Integrating Multi-Omics Data for Early Leukemia Detection." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714596.

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Sibilio, Pasquale, Federica Conte, and Paola Paci. "Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822251.

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Mehrabian, Hatef, Sangeetha Mahadevan, Michael Sharpnack, Christina Moon, and Lauri Diehl. "Integration of spatial transcriptomics and immunofluorescence staining to enable colocalized multi-omics analysis in chronic liver disease." In Digital and Computational Pathology, edited by John E. Tomaszewski and Aaron D. Ward. SPIE, 2025. https://doi.org/10.1117/12.3047319.

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Tabakhi, Sina, and Haiping Lu. "Multi-agent Feature Selection for Integrative Multi-omics Analysis." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871758.

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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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Reustle, Anna, Moreno Di Marco, Florian Büttner, et al. "Abstract 5687: Integrative -omics analysis to identify drug targets for ccRCC immunotherapy." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-5687.

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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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Peng, Tao, Kamyar Esmaeili Pourfarhangi, and Kai Tan. "Abstract PO-026: GLUER: integrative analysis of multi-omics data at single-cell resolution." In Abstracts: AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; September 17-18, 2020. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.tumhet2020-po-026.

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BRUNAK, SØREN, FRANCISCO M. DE LA VEGA, ADAM MARGOLIN, BENJAMIN J. RAPHAEL, GUNNAR RÄTSCH, and JOSHUA M. STUART. "CANCER PANOMICS: COMPUTATIONAL METHODS AND INFRASTRUCTURE FOR INTEGRATIVE ANALYSIS OF CANCER HIGH-THROUGHPUT “OMICS” DATA." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814644730_0002.

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Reports on the topic "Integrative Omics Analysis"

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Berube, Paul M., Scott M. Gifford, Bonnie Hurwitz, Bethany Jenkins, Adrian Marchetti, and Alyson E. Santoro. Roadmap Towards Communitywide Intercalibration and Standardization of Ocean Nucleic Acids ‘Omics Measurements. Woods Hole Oceanographic Institution, 2022. http://dx.doi.org/10.1575/1912/28054.

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In January 2020, the US Ocean Carbon & Biogeochemistry (OCB) Project Office funded the Ocean Nucleic Acids 'omics Intercalibration and Standardization workshop held at the University of North Carolina in Chapel Hill. Thirty-two participants from across the US, along with guests from Canada and France, met to develop a framework for standardization and intercalibration (S&I) of ocean nucleic acid ‘omics (na’omics) approaches (i.e., amplicon sequencing, metagenomics and metatranscriptomics). During the three-day workshop, participants discussed numerous topics, including: a) sample bioma

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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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