ArticleScientific reports2023
Factor-specific generative pattern from large-scale drug-induced gene expression profile.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.