ArticlePloS one2017
Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts.NAR genomics and bioinformatics · 2025Article
- Temporal GeneTerrain: advancing precision medicine through dynamic gene expression visualization.Frontiers in bioinformatics · 2025Article
- Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.NPJ precision oncology · 2024Article
- MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.Journal of integrative bioinformatics · 2024Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.Bioinformatics (Oxford, England) · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.Scientific reports · 2023Article
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Scalable transcriptomics analysis with Dask: applications in data science and machine learning.BMC bioinformatics · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.JMIR formative research · 2022Article
- Predicting Anticancer Drug Resistance Mediated by Mutations.Pharmaceuticals (Basel, Switzerland) · 2022Article
- A cross-study analysis of drug response prediction in cancer cell lines.Briefings in bioinformatics · 2022Article
- Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma.BMC cancer · 2022Article
- Dissecting the Genome for Drug Response Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.
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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.