Factor-specific generative pattern from large-scale drug-induced gene expression profile.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 04 2023
18 04 2023
Historique:
received:
06
12
2022
accepted:
06
04
2023
medline:
20
4
2023
pubmed:
19
4
2023
entrez:
18
04
2023
Statut:
epublish
Résumé
Drug discovery is a complex and interdisciplinary field that requires the identification of potential drug targets for specific diseases. In this study, we present FacPat, a novel approach that identifies the optimal factor-specific pattern explaining the drug-induced gene expression profile. FacPat uses a genetic algorithm based on pattern distance to mine the optimal factor-specific pattern for each gene in the LINCS L1000 dataset. We applied Benjamini-Hochberg correction to control the false discovery rate and identified significant and interpretable factor-specific patterns consisting of 480 genes, 7 chemical compounds, and 38 human cell lines. Using our approach, we identified genes that show context-specific effects related to chemical compounds and/or human cell lines. Furthermore, we performed functional enrichment analysis to characterize biological features. We demonstrate that FacPat can be used to reveal novel relationships among drugs, diseases, and genes.
Identifiants
pubmed: 37072452
doi: 10.1038/s41598-023-33061-x
pii: 10.1038/s41598-023-33061-x
pmc: PMC10113368
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6339Informations de copyright
© 2023. The Author(s).
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