Integrating biological data across multiple platforms using importance-weighted transfer learning and applications to breast cancer data sets

Abstract

Traditional machine learning approaches are based on the premise that the training and testing samples come from a common probability distribution. Transfer learning refers to situations where this assumption does not necessarily hold. Integrating biological data measured on diverse platforms is a major challenge. Transfer learning is a natural candidate for achieving such integration. In this paper, we adapt the ?1 — norm SVM using the importance weighting approach to fit into the paradigm of Transfer Learning under Covariate Shift, with the aim of integrating biological data sources from diverse platforms. The conditional probability of the testing data with respect to the training data is estimated using a small number of testing samples. The weights of the l 1 -norm SVM are adapted using this estimated conditional probability, also known as the importance weight. To validate our approach, we applied the proposed algorithm to the problem of classifying breast cancer tumors as ERpositive or ER-negative, which is the first step in personalizing therapy to the patient. Then we compared it against conversion to Z-scores, which is the current best practice. The l 1 -norm SVM modified via importance weighting shows better performance than using Z-scores, on five different test data sets.