Adversarial Inductive Transfer Learning with input and output space adaptation

Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin C. Collins, Martin Ester

Abstract: We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains. AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies. Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information. The 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) and clinical datasets. Discrepancies exist between 1) the genomic data of pre-clinical and clinical datasets (the input space), and 2) the different measures of the drug response (the output space). To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

Code: https://github.com/tllabtl/AITL

Keywords: Inductive transfer learning, adversarial learning, multi-task learning, pharmacogenomics, precision oncology

TL;DR: A novel method of inductive transfer learning that employs adversarial learning and multi-task learning to address the discrepancy in input and output space