ArticleIET systems biology2020
Ensembled machine learning framework for drug sensitivity prediction.
Article in IET systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Innovative strategies to overcome cancer drug resistance: from nanodelivery systems to targeted therapeutics.Discover oncology · 2026Review
- Exploring Influencing Factors of Medication Adherence Among Chinese Patients With Alzheimer Disease: Delphi Study Informing Future Artificial Intelligence-Supported Interventions.JMIR formative research · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Article
- Review
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- Ensemble transfer learning for the prediction of anti-cancer drug response.Scientific reports · 2020Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums have contributed to providing open access to pharmacogenomic data. These databases have helped in developing computational approaches for drug sensitivity prediction. Cancer is a complex disease involving the heterogeneous behaviour of same tumour-type patients towards the same kind of drug therapy. Several methods have been proposed in the literature to predict drug sensitivity. However, these methods are not efficient enough to predict drug sensitivity. The present study has proposed an ensemble learning framework for drug-response prediction using a modified rotation forest. The proposed framework is further compared with three state-of-the-art algorithms and two baseline methods using Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) drug screens. The authors have also predicted missing drug response values in the data set using the proposed approach. The proposed approach outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.14 and 0.404 is achieved using GDSC and CCLE drug screens, respectively. The obtained results show that the proposed framework has considerable potential to improve anti-cancer drug response prediction.
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