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- W4366266956 abstract "Abstract 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." @default.
- W4366266956 created "2023-04-20" @default.
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- W4366266956 date "2023-04-18" @default.
- W4366266956 modified "2023-09-25" @default.
- W4366266956 title "Factor-specific generative pattern from large-scale drug-induced gene expression profile" @default.
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- W4366266956 doi "https://doi.org/10.1038/s41598-023-33061-x" @default.
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