Network-based approaches for modeling disease regulation and progression.
Disease modeling
Network enrichment
Network inference
Network medidince
Systems medicine
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2023
2023
Historique:
received:
15
08
2022
revised:
14
12
2022
accepted:
14
12
2022
entrez:
26
1
2023
pubmed:
27
1
2023
medline:
27
1
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36698974
doi: 10.1016/j.csbj.2022.12.022
pii: S2001-0370(22)00579-7
pmc: PMC9841310
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
780-795Informations de copyright
© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Déclaration de conflit d'intérêts
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.
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