Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients

Turki Turki, Zhi Wei, Jason T.L. Wang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Traditional machine learning approaches to drug sensitivity prediction assume that training data and test data must be in the same feature space and have the same underlying distribution. However, in real-world applications, this assumption does not hold. For example, we sometimes have limited training data for the task of drug sensitivity prediction in multiple myeloma patients (target task), but we have sufficient auxiliary data for the task of drug sensitivity prediction in patients with another cancer type (related task), where the auxiliary data for the related task are in a different feature space or have a different distribution. In such cases, transfer learning, if applied correctly, would improve the performance of prediction algorithms on the test data of the target task via leveraging the auxiliary data from the related task. In this paper, we present two transfer learning approaches that combine the auxiliary data from the related task with the training data of the target task to improve the prediction performance on the test data of the target task. We evaluate the performance of our transfer learning approaches exploiting three auxiliary data sets and compare them against baseline approaches using the area under the receiver operating characteristic curve on the test data of the target task. Experimental results demonstrate the good performance of our approaches and their superiority over the baseline approaches when auxiliary data are incorporated.

Original languageEnglish (US)
Article number7907277
Pages (from-to)7381-7393
Number of pages13
JournalIEEE Access
Volume5
DOIs
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Keywords

  • Machine learning
  • cancer drug discovery
  • clinical informatics
  • data mining
  • precision medicine

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