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Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology.

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Názov: Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology.
Autori: Kant S; Department of Biotechnology, School of Biotechnology and Biosciences, Brainware University, Kolkata, India., Deepika; Gautam Buddha Mahila College, Magadh University, Gaya, India., Roy S; Department of Pharmaceutical Technology, School of Health and Medical Sciences, Adamas University, Kolkata, India.
Zdroj: Omics : a journal of integrative biology [OMICS] 2025 Dec; Vol. 29 (12), pp. 576-587. Date of Electronic Publication: 2025 Nov 07.
Spôsob vydávania: Journal Article; Review
Jazyk: English
Informácie o časopise: Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 101131135 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8100 (Electronic) Linking ISSN: 15362310 NLM ISO Abbreviation: OMICS Subsets: MEDLINE
Imprint Name(s): Original Publication: Larchmont, NY : Mary Ann Liebert, Inc., c2002-
Výrazy zo slovníka MeSH: Systems Biology*/methods , Artificial Intelligence* , Genomics*/methods, Humans ; Metabolomics/methods ; Proteomics/methods ; Synthetic Biology/methods ; Computational Biology/methods ; Machine Learning ; Multiomics
Abstrakt: The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.
Contributed Indexing: Keywords: artificial intelligence; data integration; multi-omics; synthetic biology; systems biology
Entry Date(s): Date Created: 20251107 Date Completed: 20251204 Latest Revision: 20251204
Update Code: 20251204
DOI: 10.1177/15578100251392371
PMID: 41203247
Databáza: MEDLINE
Popis
Abstrakt:The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.
ISSN:1557-8100
DOI:10.1177/15578100251392371