Clinical intelligence: New machine learning techniques for predicting clinical drug response

Document Type

Article

Publication Date

4-1-2019

Abstract

Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical drug response correctly, clinicians are able to understand patient-to-patient differences in drug sensitivity outcomes, which in turn results in lesser time spent and lower cost associated with identifying effective drug candidates. Although technological advances in high-throughput drug screening in cells led to the generation of a substantial amount of relevant data, the analysis of such data would be a challenging task. There is a critical need for advanced machine learning (ML) algorithms to generate accurate predictions of clinical drug response. A major goal of this work is to provide advanced ML tools to data analysts, who would in turn build prediction calculators to be incorporated into intelligent clinical decision support systems. Such innovative tools could be used to enhance patient-care, among other uses. To achieve this goal, we develop new ML techniques, including a transfer learning approach coupled with or without a boosting technique. Experimental results on real clinical data pertaining to breast cancer, multiple myeloma, and triple-negative cancer patients demonstrate the effectiveness and superiority of the proposed approaches compared to baseline approaches, including existing transfer learning methods.

Identifier

85061373671 (Scopus)

Publication Title

Computers in Biology and Medicine

External Full Text Location

https://doi.org/10.1016/j.compbiomed.2018.12.017

e-ISSN

18790534

ISSN

00104825

PubMed ID

30771879

First Page

302

Last Page

322

Volume

107

Grant

G-121-611-39

Fund Ref

Department of Sport and Recreation, Government of Western Australia

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