Benchmarking machine learning methods for synthetic lethality prediction in cancer.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
20 Oct 2024
20 Oct 2024
Historique:
received:
27
11
2023
accepted:
23
09
2024
medline:
21
10
2024
pubmed:
21
10
2024
entrez:
20
10
2024
Statut:
epublish
Résumé
Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.
Identifiants
pubmed: 39428397
doi: 10.1038/s41467-024-52900-7
pii: 10.1038/s41467-024-52900-7
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9058Informations de copyright
© 2024. The Author(s).
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