A transfer learning approach for integrating biological data across platforms

Abstract

Transfer learning refers to situations where a classifier is trained on one set of data and tested on another set of data that may have an entirely different probability distribution. Biological data derived from diverse platforms, and possibly using diverse technologies, is a natural candidate for applying transfer learning methodologies. In this paper, we adapt the l1-norm SVM to fit into the paradigm of Transfer Learning, by using the importance weighting approach. Our aim is to integrate biological data from diverse platforms. To validate our approach, we applied the proposed algorithm to the problem of classifying breast cancer tumors as Estrogen- Receptor-positive (ER-positive) or Estrogen-Receptor-negative (ER-negative), which is the first step in personalizing therapy to the patient. The standard approach used in Biology is to convert data to Z-scores, that is, to subtract the mean and divide by the standard deviation. The algorithm proposed here shows better performance than using Z-scores to account for platform variations.