A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40424358", "kind": "science-record", "revision": 2, "content_hash": "sha256:aca62b984f9915995e91096f044e2dcf907774896fa3244324ce4cf222d05174", "created_at": "2026-11-01T20:30:33Z", "updated_at": "2026-11-01T21:35:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.", "doi": "10.1093/bioinformatics/btaf319", "pmid": "40424358", "authors": ["Haochen Zhao", "Dingxi Li", "Jian Zhong", "Xiao Liang", "Guihua Duan", "Jianxin Wang"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the population, which can guide patient medication use and drug development. Many computational methods have been developed to predict the frequency of drug side effects as an alternative to clinical trials. However, existing methods typically build regression models on five frequency classes of drug side effects and tend to overfit the training set, leading to boundary handling issues and the risk of overfitting.\nRESULTS: To address this problem, we develop a multi-source similarity fusion-based model, named multi-source similarity fusion (MSSF), for predicting five frequency classes of drug side effects. Compared to existing methods, our model utilizes the multi-source feature fusion module and the self-attention mechanism to explore the relationships between drugs and side effects deeply and employs Bayesian variational inference to more accurately predict the frequency classes of drug side effects. The experimental results indicate that MSSF consistently achieves superior performance compared to existing models across multiple evaluation settings, including cross-validation, cold-start experiments, and independent testing. The visual analysis and case studies further demonstrate MSSF's reliable feature extraction capability and promise in predicting the frequency classes of drug side effects.\nAVAILABILITY AND IMPLEMENTATION: The source code of MSSF is available on GitHub (https://github.com/dingxlcse/MSSF.git) and archived on Zenodo (DOI: 10.5281/zenodo.15462041).", "claims": [{"id": "claim-method", "statement": "Meanwhile, we conduct paired t-tests between the results of each fold in the ten-fold cross-validation of MSSF and the corresponding results of other models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40424358", "locator": "sections[13] \"3.1 Comparison with other methods\" paragraph 2", "quote": "Meanwhile, we conduct paired t-tests between the results of each fold in the ten-fold cross-validation of MSSF and the corresponding results of other models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary data is available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40424358", "locator": "sections[22] \"Supplementary data\" paragraph 2", "quote": "Supplementary data is available at Bioinformatics online."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40424358"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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