Article

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

2020-01-25

Abstract excerpt

<h4>Motivation</h4> The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a targ...

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Literature Corpus work: 1b45da8e-cada-5a2f-ace0-865abf83defc
DOI: 10.1101/2020.01.24.918953

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