TY - JOUR
T1 - Development and evaluation of a java-based deep neural network method for drug response predictions
AU - Huang, Beibei
AU - Fong, Lon W.R.
AU - Chaudhari, Rajan
AU - Zhang, Shuxing
N1 - Publisher Copyright:
Copyright © 2023 Huang, Fong, Chaudhari and Zhang.
PY - 2023
Y1 - 2023
N2 - Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r2 as high as 0.81.
AB - Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r2 as high as 0.81.
KW - artificial intelligence (AI)
KW - deep learning
KW - deep neural network
KW - drug response
KW - multilayer neural network (MNN)
KW - quantitative structure activity relationship (QSAR)
KW - triple-negative breast cancer (TNBC)
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U2 - 10.3389/frai.2023.1069353
DO - 10.3389/frai.2023.1069353
M3 - Article
C2 - 37035534
AN - SCOPUS:85153370462
SN - 2624-8212
VL - 6
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
M1 - 1069353
ER -