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Journal articles on the topic 'Integrative Omics Analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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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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Rotroff, Daniel M., and Alison A. Motsinger-Reif. "Embracing Integrative Multiomics Approaches." International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.

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As “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area.

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Jeon, Jaemin, Eon Yong Han, and Inuk Jung. "MOPA: An integrative multi-omics pathway analysis method for measuring omics activity." PLOS ONE 18, no. 3 (2023): e0278272. http://dx.doi.org/10.1371/journal.pone.0278272.

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Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mE

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Singh, Amrit, Casey P. Shannon, Benoît Gautier, et al. "DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays." Bioinformatics 35, no. 17 (2019): 3055–62. http://dx.doi.org/10.1093/bioinformatics/bty1054.

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Abstract Motivation In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. Results Using simulations and benchmark multi-omics studies, we show that DIABLO identifies featu

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Xu, Chao, Ji-Gang Zhang, Dongdong Lin, Lan Zhang, Hui Shen, and Hong-Wen Deng. "A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease." G3 Genes|Genomes|Genetics 7, no. 7 (2017): 2271–79. http://dx.doi.org/10.1534/g3.117.042408.

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Abstract Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from div

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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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Park, Mira, Doyoen Kim, Kwanyoung Moon, and Taesung Park. "Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components." International Journal of Molecular Sciences 21, no. 21 (2020): 8202. http://dx.doi.org/10.3390/ijms21218202.

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The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analy

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Cai, Kexin, Yuqing Luo, Hongyin Chen, et al. "Integrative omics analysis identifies biomarkers of septic cardiomyopathy." PLOS ONE 19, no. 11 (2024): e0310412. http://dx.doi.org/10.1371/journal.pone.0310412.

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Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were

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Zhao, Qing, Xingjie Shi, Jian Huang, Jin Liu, Yang Li, and Shuangge Ma. "Integrative analysis of ‘-omics’ data using penalty functions." Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 1 (2014): 99–108. http://dx.doi.org/10.1002/wics.1322.

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Dong, Xianjun, Chunyu Liu, and Mikhail Dozmorov. "Review of multi-omics data resources and integrative analysis for human brain disorders." Briefings in Functional Genomics 20, no. 4 (2021): 223–34. http://dx.doi.org/10.1093/bfgp/elab024.

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Abstract In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We fu

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Chang, Sheng-Mao, Meng Yang, Wenbin Lu, et al. "Gene-set integrative analysis of multi-omics data using tensor-based association test." Bioinformatics 37, no. 16 (2021): 2259–65. http://dx.doi.org/10.1093/bioinformatics/btab125.

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Abstract Motivation Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. Results We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for t

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Shoaib, A. S. M., Nourin Nishat, Muniroopesh Raasetti, and Imran Arif. "INTEGRATIVE MACHINE LEARNING APPROACHES FOR MULTI-OMICS DATA ANALYSIS IN CANCER RESEARCH." Global Mainstream Journal 1, no. 2 (2024): 26–39. http://dx.doi.org/10.62304/ijhm.v1i2.149.

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Integrative machine learning approaches have emerged as essential tools in the analysis of multi-omics data in cancer research, offering significant advancements in understanding complex biological systems. This review emphasizes recent progress in these techniques, highlighting their ability to manage the complexity and heterogeneity of multi-omics datasets, which include genomics, transcriptomics, proteomics, and metabolomics. By effectively integrating these diverse data types, machine learning approaches provide unprecedented insights into cancer mechanisms, facilitating the discovery of n

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Jendoubi, Takoua. "Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer." Metabolites 11, no. 3 (2021): 184. http://dx.doi.org/10.3390/metabo11030184.

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Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, eluci

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Jiang, Min-Zhi, François Aguet, Kristin Ardlie, et al. "Canonical correlation analysis for multi-omics: Application to cross-cohort analysis." PLOS Genetics 19, no. 5 (2023): e1010517. http://dx.doi.org/10.1371/journal.pgen.1010517.

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Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has no

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Robinson, Jonathan L., and Jens Nielsen. "Integrative analysis of human omics data using biomolecular networks." Molecular BioSystems 12, no. 10 (2016): 2953–64. http://dx.doi.org/10.1039/c6mb00476h.

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Labarga, Alberto, Judith Martínez-Gonzalez, and Miguel Barajas. "Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine." Biology 13, no. 5 (2024): 338. http://dx.doi.org/10.3390/biology13050338.

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Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.

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Lin, Dongdong, Hima B. Yalamanchili, Xinmin Zhang, et al. "CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines." PLOS Computational Biology 16, no. 12 (2020): e1008498. http://dx.doi.org/10.1371/journal.pcbi.1008498.

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Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysi

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Guo, Youming, Lingling Luo, Jing Zhu, and Chengrang Li. "Multi-Omics Research Strategies for Psoriasis and Atopic Dermatitis." International Journal of Molecular Sciences 24, no. 9 (2023): 8018. http://dx.doi.org/10.3390/ijms24098018.

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Psoriasis and atopic dermatitis (AD) are multifactorial and heterogeneous inflammatory skin diseases, while years of research have yielded no cure, and the costs associated with caring for people suffering from psoriasis and AD are a huge burden on society. Integrating several omics datasets will enable coordinate-based simultaneous analysis of hundreds of genes, RNAs, chromatins, proteins, and metabolites in particular cells, revealing networks of links between various molecular levels. In this review, we discuss the latest developments in the fields of genomes, transcriptomics, proteomics, a

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Kogelman, Lisette J. A., Madeleine Ernst, Katrine Falkenberg, et al. "Multi-omics to predict changes during cold pressor test." BMC Genomics 23, no. 1 (2022): 759. https://doi.org/10.1186/s12864-022-08981-z.

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<strong>Background: </strong>The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each 'omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least sq

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Ramiro, Laura, Teresa García-Berrocoso, Ferran Briansó, et al. "Integrative Multi-omics Analysis to Characterize Human Brain Ischemia." Molecular Neurobiology 58, no. 8 (2021): 4107–21. http://dx.doi.org/10.1007/s12035-021-02401-1.

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Park, Heewon, Atushi Niida, Satoru Miyano, and Seiya Imoto. "Sparse Overlapping Group Lasso for Integrative Multi-Omics Analysis." Journal of Computational Biology 22, no. 2 (2015): 73–84. http://dx.doi.org/10.1089/cmb.2014.0197.

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Nishimura, Yuhei. "Integrative analysis of public omics databases for drug discovery." Proceedings for Annual Meeting of The Japanese Pharmacological Society 92 (2019): 2—AS2–1. http://dx.doi.org/10.1254/jpssuppl.92.0_2-as2-1.

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Kim, SungHwan, Dongwan Kang, Zhiguang Huo, Yongseok Park, and George C. Tseng. "Meta-analytic principal component analysis in integrative omics application." Bioinformatics 34, no. 8 (2017): 1321–28. http://dx.doi.org/10.1093/bioinformatics/btx765.

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Yoo, Seungyeul, Tao Huang, Joshua D. Campbell, et al. "MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis." PLoS Computational Biology 10, no. 8 (2014): e1003790. http://dx.doi.org/10.1371/journal.pcbi.1003790.

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Huang, Jiajun, Ze Long, Wanjun Lin, et al. "Integrative omics analysis of p53-dependent regulation of metabolism." FEBS Letters 592, no. 3 (2018): 380–93. http://dx.doi.org/10.1002/1873-3468.12968.

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Rosa, María Martínez-Espinosa. "Impact of the "Omics Sciences" in Medicine: New Era for Integrative Medicine." Journal of Clinical Microbiology and Biochemical Technolog 3, no. 1 (2017): 009–13. https://doi.org/10.17352/jcmbt.000018.

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<strong>Background and objective: </strong>This work collects and analyses information about the evolution of medical practice during the last centuries. The main aim is to summarise new insights on “omics sciences” and their impact in medicine. <strong>Methods: </strong>Use of appropriate keywords, use of different engines of research information and the subsequent bibliometric analysis of the information. <strong>Results: </strong>The impact of the “omics sciences” in the early diagnosis of diseases is highly significant. Consequently, the implementation of “omi

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Kliuchnikova, Anna, Arina Gordeeva, Aziz Abdurakhimov, et al. "Ovarian Cancer: Multi-Omics Data Integration." International Journal of Molecular Sciences 26, no. 13 (2025): 5961. https://doi.org/10.3390/ijms26135961.

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This study focuses on the systematization and integration of ovarian cancer multi-omics data, revealing patterns in the application of different omics-based approaches and assessing factors that affect the identification of potential biomarkers. An integrative analysis of 51 publications revealed 1649 potential biomarkers. The findings emphasized the molecular diversity of ovarian cancer. They demonstrated the importance of performing the comprehensive integration of molecular and clinical data to search for diagnostic alternatives and molecular patterns underlying ovarian cancer. The heteroge

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Sun, Tao, Mengci Li, Xiangtian Yu, et al. "3MCor: an integrative web server for metabolome–microbiome-metadata correlation analysis." Bioinformatics 38, no. 5 (2021): 1378–84. http://dx.doi.org/10.1093/bioinformatics/btab818.

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Abstract Motivation The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated networ

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Ghosh, Sreemoyee, Chiara Pastrello, Omar Correa, et al. "Identification of Psoriatic Arthritis-Related Pathways Using Multi-Omics Data Integration." Journal of Rheumatology 52, Suppl 2 (2025): 73–74. https://doi.org/10.3899/jrheum.2025-0314.60.

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ObjectivesThe objectives of this study include (i) identifying and curating publicly available omics studies on psoriatic disease (PsD) to build a multiomics data integration portal (PsDIP) and (ii) integrating studies from PsDIP comparing the serum omics profiles of psoriatic arthritis (PsA) and cutaneous psoriasis (PsC) patients to identify PsA-related pathways.MethodsA scoping review was conducted to curate all publicly available omics studies in the field of PsD from 3 databases: Ovid MEDLINE, Embase and Cochrane Central. Inclusion criteria comprise all English-language studies related to

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Yet, İdil. "Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis." Acta Medica 55, no. 3 (2024): 184–89. http://dx.doi.org/10.32552/2024.actamedica.1028.

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Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivi

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Khalyfa, Abdelnaby, Jose M. Marin, David Sanz-Rubio, Zhen Lyu, Trupti Joshi, and David Gozal. "Multi-Omics Analysis of Circulating Exosomes in Adherent Long-Term Treated OSA Patients." International Journal of Molecular Sciences 24, no. 22 (2023): 16074. http://dx.doi.org/10.3390/ijms242216074.

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Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent

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Pierre-Jean, Morgane, Jean-François Deleuze, Edith Le Floch, and Florence Mauger. "Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration." Briefings in Bioinformatics 21, no. 6 (2019): 2011–30. http://dx.doi.org/10.1093/bib/bbz138.

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Abstract Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim t

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Lu, Yihao, Meritxell Oliva, Brandon L. Pierce, Jin Liu, and Lin S. Chen. "Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-46675-0.

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AbstractGenetic effects on functionally related ‘omic’ traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among om

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"Integrative analysis of omics data." Methods 124 (July 2017): 1–2. http://dx.doi.org/10.1016/j.ymeth.2017.07.016.

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Agamah, Francis E., Jumamurat R. Bayjanov, Anna Niehues, et al. "Computational approaches for network-based integrative multi-omics analysis." Frontiers in Molecular Biosciences 9 (November 14, 2022). http://dx.doi.org/10.3389/fmolb.2022.967205.

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Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches a

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Das, Sarmistha, and Indranil Mukhopadhyay. "TiMEG: an integrative statistical method for partially missing multi-omics data." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-03034-z.

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AbstractMulti-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Her

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Itai, Yonatan, Nimrod Rappoport, and Ron Shamir. "Integration of gene expression and DNA methylation data across different experiments." Nucleic Acids Research, July 3, 2023. http://dx.doi.org/10.1093/nar/gkad566.

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Abstract Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the tw

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Zhang, Qiang, Xiang-He Meng, Chuan Qiu, et al. "Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans." Human Genomics 16, no. 1 (2022). http://dx.doi.org/10.1186/s40246-022-00388-x.

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Abstract Background Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels. Subjects and methods Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molec

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Sun, Wenli, and Mohamad Hesam Shahrajabian. "Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application." Current Pharmaceutical Analysis 19 (April 6, 2023). http://dx.doi.org/10.2174/1573412919666230406100948.

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Abstract: Multi-omics approaches have developed as a profitable technique for plant systems, a popular method in medical and biological sciences underlining the necessity to outline new integrative technology and functions to facilitate the multi-scale depiction of biological systems. Understanding a biological system through various omics layers reveals supplementary sources of variability and probably inferring the sequence of cases leading to a definitive process. Manuscripts and reviews were searched on PubMed with the keywords of multi-omics, data analysis, omics, data analysis, data inte

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Hagenbeek, Fiona A., Jenny van Dongen, René Pool, et al. "Integrative Multi-omics Analysis of Childhood Aggressive Behavior." Behavior Genetics, November 7, 2022. http://dx.doi.org/10.1007/s10519-022-10126-7.

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AbstractThis study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-om

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Nakayasu, Ernesto S., Carrie D. Nicora, Amy C. Sims, et al. "MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses." mSystems 1, no. 3 (2016). http://dx.doi.org/10.1128/msystems.00043-16.

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ABSTRACT In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based ext

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