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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kai Tian Mingyu Shao Shuigeng Zhou Jihong Guan |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Comput. Sci. & Technol., Tongji Univ., Shanghai, China (Jihong Guan) || Shanghai Key Lab. of Intell. Inf. Process., Fudan Univ., Shanghai, China (Kai Tian; Mingyu Shao; Shuigeng Zhou) |
| 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 many 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 Deep Learning for Compound-Protein Interactions 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. |
| Sponsorship | IEEE |
| Starting Page | 29 |
| Ending Page | 34 |
| File Size | 1550927 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467367998 |
| DOI | 10.1109/BIBM.2015.7359651 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-11-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radio frequency Training Deep learning Genomics Deep neural network (DNN) Bioinformatics Compound-protein interaction |
| Content Type | Text |
| Resource Type | Article |
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