Synthetic lethal gene pairs: Experimental approaches and predictive models.
computational biology
gene–gene interaction
machine learning (ML)
predictive model
synthetic lethality
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2022
2022
Historique:
received:
05
06
2022
accepted:
07
11
2022
entrez:
19
12
2022
pubmed:
20
12
2022
medline:
20
12
2022
Statut:
epublish
Résumé
Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.
Identifiants
pubmed: 36531238
doi: 10.3389/fgene.2022.961611
pii: 961611
pmc: PMC9751344
doi:
Types de publication
Systematic Review
Langues
eng
Pagination
961611Informations de copyright
Copyright © 2022 Tang, Gökbağ, Fan, Shao, Huo, Wu, Cheng and Li.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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