Drug sensitivity prediction framework using ensemble and multi-task learning

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KSRMF: Kernelized similarity based regularized matrix factorization framework for predicting anti-cancer drug responses

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An Integrated Framework for Identification of Effective and Synergistic Anti-Cancer Drug Combinations

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An Optimized Framework for Cancer Classification Using Deep Learning and Genetic Algorithm

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Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine

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Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

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Optimal Drug Prediction From Personal Genomics Profiles

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Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning

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A community effort to assess and improve drug sensitivity prediction algorithms

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An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge

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Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction

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Genomics-Driven Oncology: Framework for an Emerging Paradigm

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Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

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Modeling precision treatment of breast cancer

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Systematic identification of genomic markers of drug sensitivity in cancer cells

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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

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Nilotinib is effective in patients with chronic myeloid leukemia in chronic phase after imatinib resistance or intolerance: 24-month follow-up results

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Clinical Implications of the Cancer Genome

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Convex multi-task feature learning

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