Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
| Autor: | Henry Gerdes, David Britton, Maruan Hijazi, Nosheen Akhtar, Jon Travers, Pedro R. Cutillas, Shirin Elizabeth Khorsandi, Arran Dokal, Jude Fitzgibbon, Ruth Osuntola, Pedro Casado, Vinothini Rajeeve |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Drug Proteomics Mean squared error Computer science media_common.quotation_subject Science Predictive medicine General Physics and Astronomy Antineoplastic Agents Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Article Machine Learning 03 medical and health sciences 0302 clinical medicine Text mining Robustness (computer science) Cell Line Tumor Neoplasms medicine Humans media_common Cell Proliferation Cancer Multidisciplinary Leukemia business.industry Rank (computer programming) Cytarabine Computational Biology General Chemistry Hep G2 Cells medicine.disease Prognosis Log-rank test 030104 developmental biology Ranking 030220 oncology & carcinogenesis Artificial intelligence Drug Screening Assays Antitumor business computer |
| Zdroj: | Nature Communications Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021) |
| ISSN: | 2041-1723 |
| Popis: | Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p Artificial intelligence and machine learning promise to transform cancer therapies by accurately predicting the most appropriate drugs to treat individual patients. Here, the authors present an approach which uses omics data to produce ordered lists of drugs based on their effectiveness in decreasing cancer cell proliferation. |
| Databáze: | OpenAIRE |
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