651. Boosting compound-protein interaction prediction by deep learning.
- Author
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Tian, Kai, Shao, Mingyu, Wang, Yang, Guan, Jihong, and Zhou, Shuigeng
- Subjects
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DEEP learning , *BIOLOGICAL databases , *PHARMACOLOGY , *ARTIFICIAL neural networks , *COMPUTERS in biology - Abstract
The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of D eep L earning for C ompound- P rotein I nteractions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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