ArticleBMC bioinformatics2021
Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 33 citations in OpenAlex.
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.Scientific reports · 2025Article
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.PLoS computational biology · 2024Article
- KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.iScience · 2024Article
- Article
- Acacetin exerts antitumor effects on gastric cancer by targeting EGFR.Frontiers in pharmacology · 2023Article
- Hepatocellular Carcinoma Chemoprevention with Generic Agents.Seminars in liver disease · 2022Review
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- "Big Data" Approaches for Prevention of the Metabolic Syndrome.Frontiers in genetics · 2022Review
- On the robustness of generalization of drug-drug interaction models.BMC bioinformatics · 2021Article
- Revealing the Mechanism of Friedelin in the Treatment of Ulcerative Colitis Based on Network Pharmacology and Experimental Verification.Evidence-based complementary and alternative medicine : eCAM · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4).
resultsThe proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo.
conclusionsThe proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.
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