TY - JOUR
T1 - Clinical intelligence
T2 - New machine learning techniques for predicting clinical drug response
AU - Turki, Turki
AU - Wang, Jason T.L.
N1 - Funding Information:
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University , Jeddah, under grant no. G-121-611-39 . The authors, therefore, acknowledge with thanks DSR for technical and financial support.
Funding Information:
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. G-121-611-39. The authors, therefore, acknowledge with thanks DSR for technical and financial support.
Funding Information:
Turki Turki received the B.S. degree in computer science from King Abdulaziz University, the M.S. degree in computer science from NYU.POLY, and the Ph.D. degree in computer science from the New Jersey Institute of Technology. He is currently an assistant professor with the Department of Computer Science, King Abdulaziz University, Saudi Arabia. His research interests include algorithms, machine learning, data mining, big data analytics, sustainable computing, health informatics, bioinformatics, computational biology, and social networks. His works have been published in journals such as BMC Genomics, BMC Systems Biology, IEEE Access, BioMed Research International, Journal of Bioinformatics and Computational Biology, Computers in Biology and Medicine, and Genes. He was presented with the distinction award from the deanship of scientific research at the King Abdulaziz University. He is supported by King Abdulaziz University and is currently working on several biomedicine related projects. Dr. Turki has served on the program committees of several international conferences. Also, he is an editorial board member of Computers in Biology and Medicine and Sustainable Computing: Informatics and Systems.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Applications in biology and medicine
KW - Drug discovery
KW - Drug sensitivity
KW - Machine learning
KW - Transfer learning
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U2 - 10.1016/j.compbiomed.2018.12.017
DO - 10.1016/j.compbiomed.2018.12.017
M3 - Article
C2 - 30771879
AN - SCOPUS:85061373671
SN - 0010-4825
VL - 107
SP - 302
EP - 322
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -