ArticleBMC bioinformatics2022
Machine learning approach informs biology of cancer drug response.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Application progress of artificial intelligence in managing thyroid disease.Frontiers in endocrinology · 2025Review
- Article
- Understanding cancer drug resistance withiScience · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Molecular features and predictive models identify the most lethal subtype and a therapeutic target for osteosarcoma.Frontiers in oncology · 2023Article
- SRC-RAC1 signaling drives drug resistance to BRAF inhibition in de-differentiated cutaneous melanomas.NPJ precision oncology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundThe mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets.
methodsWe present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of BRAF
resultsOur method implicated lipid metabolism and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response.
conclusionsOur results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response.
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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.