ArticleBioinformatics (Oxford, England)2016
Drug-induced adverse events prediction with the LINCS L1000 data.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
- Machine learning-based prediction and external validation of treatment-related myelosuppression in patients with non-small cell lung cancer receiving PD-1 inhibitors plus platinum-doublet chemotherapy.Journal of thoracic disease · 2026Article
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Real-world performance of the machine learning-based prediction of chemotherapy-associated adverse effects in lung cancer.Oncology letters · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Review
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- Cell-type-agnostic differential gene expression uncovers conserved principles of cellular regulation.bioRxiv : the preprint server for biology · 2025Article
- Exploration of a prognostic signature for mitochondria-related genes and the therapeutic prospects of vorinostat in clear cell renal cell carcinoma.Translational andrology and urology · 2025Article
- Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study.Frontiers in pharmacology · 2025Article
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025Article
- Learning Molecular Representation in a Cell.ArXiv · 2024Article
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- From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.Molecular biology of the cell · 2024Article
- Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank.Journal of chemical information and modeling · 2024Article
- Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database.Current drug targets · 2024Review
- DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes.Bioinformatics (Oxford, England) · 2023Article
- Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.bioRxiv : the preprint server for biology · 2023Article
- A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.Bioinformatics (Oxford, England) · 2023Article
33 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
motivationAdverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections. AVAILABILITY AND IMPLEMENTATION: The interface for the adverse event predictions for the >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 OpenQuestion graph.