Synergistic drug combination prediction in multi-input neural network.
10.7507/1001-5515.201907049
- Author:
Xi CHEN
1
,
2
;
Yufang QIN
1
,
2
;
Ming CHEN
1
,
2
;
Chongyang ZHANG
1
,
2
Author Information
1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, P.R.China
2. Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai 201306, P.R.China.
- Publication Type:Journal Article
- Keywords:
anticancer drugs;
deep learning;
drug combination;
gene expression
- MeSH:
Antineoplastic Agents;
Computational Biology;
Drug Combinations;
Humans;
Neoplasms;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2020;37(4):676-682
- CountryChina
- Language:Chinese
-
Abstract:
Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.
