ArticleNature communications2024
Drug target prediction through deep learning functional representation of gene signatures.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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
49 citing papers in PubMed.
- VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug-target hypotheses.Bioinformatics (Oxford, England) · 2026Article
- Multi-layered target identification strategy of natural products: Daphnetin targets NQO1 and OPLAH in silicosis alleviation.Journal of pharmaceutical analysis · 2026Article
- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network.Bioinformatics (Oxford, England) · 2026Article
- Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.Bioinformatics (Oxford, England) · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Discovery of TYR inhibitors from de novo molecular generation to dual-track lead optimization: "Competition" between AI and chemists.Science advances · 2026Article
- Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.Analytical chemistry · 2026Article
- Review
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- A network medicine framework for multi-modal data integration in therapeutic target discovery.Communications chemistry · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.bioRxiv : the preprint server for biology · 2026Article
- Review
- Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.
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.