Network-based approaches for modeling disease regulation and progression.

Autor:	Galindez G; Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.; Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany., Sadegh S; Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.; Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany., Baumbach J; Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.; Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark., Kacprowski T; Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.; Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany., List M; Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2022 Dec 16; Vol. 21, pp. 780-795. Date of Electronic Publication: 2022 Dec 16 (Print Publication: 2023).
DOI:	10.1016/j.csbj.2022.12.022
Abstrakt:	Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Databáze:	MEDLINE
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