ArticleBriefings in bioinformatics2021
Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- BCheM: a framework integrating molecular attributes and behavioral representations for precise prediction of drug resistance across multiple disease contexts.Frontiers in bioinformatics · 2026Article
- Article
- Enhancing molecular property prediction through data integration and consistency assessment.Journal of cheminformatics · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Targeting Ferroptosis in Rare Neurological Disorders Including Pediatric Conditions: Innovations and Therapeutic Challenges.Biomedicines · 2025Review
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer.Nature communications · 2024Article
- A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method.Molecular diversity · 2024Article
- Optimal fusion of genotype and drug embeddings in predicting cancer drug response.Briefings in bioinformatics · 2024Article
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Article
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.Scientific reports · 2023Article
- Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?Bioinformatics (Oxford, England) · 2023Article
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predicting the response of cancer cell lines to specific drugs is one of the central problems in personalized medicine, where the cell lines show diverse characteristics. Researchers have developed a variety of computational methods to discover associations between drugs and cell lines, and improved drug sensitivity analyses by integrating heterogeneous biological data. However, choosing informative data sources and methods that can incorporate multiple sources efficiently is the challenging part of successful analysis in personalized medicine. The reason is that finding decisive factors of cancer and developing methods that can overcome the problems of integrating data, such as differences in data structures and data complexities, are difficult. In this review, we summarize recent advances in data integration-based machine learning for drug response prediction, by categorizing methods as matrix factorization-based, kernel-based and network-based methods. We also present a short description of relevant databases used as a benchmark in drug response prediction analyses, followed by providing a brief discussion of challenges faced in integrating and interpreting data from multiple sources. Finally, we address the advantages of combining multiple heterogeneous data sources on drug sensitivity analysis by showing an experimental comparison. Contact: betul.guvenc@aalto.fi.
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