Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66229
Title: Large-scale prediction of drug-target interactions from deep representations
Authors: Hu, PW
Chan, KCC
You, ZH
Keywords: Autoencoder
Deep representations
Drug-target interaction
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the International Joint Conference on Neural Networks, 2016, v. 2016-October, 7727339, p. 1236-1243 How to cite?
Abstract: Identifying drug-target interactions (DTIs) is a major challenge in drug development. Traditionally, similarity-based methods use drug and target similarity matrices to infer the potential drug-target interactions. But these techniques do not handle biochemical data directly. While recent feature-based methods reveal simple patterns of physicochemical properties, efficient method to study large interactive features and precisely predict interactions is still missing. Deep learning has been found to be an appropriate tool for converting high-dimensional features to low-dimensional representations. These deep representations generated from drug-protein pair can serve as training examples for the interaction predictor. In this paper, we propose a promising approach called multi-scale features deep representations inferring interactions (MFDR). We extract the large-scale chemical structure and protein sequence descriptors so as to machine learning model predict if certain human target protein can interact with a specific drug. MFDR use Auto-Encoders as building blocks of deep network for reconstruct drug and protein features to low-dimensional new representations. Then, we make use of support vector machine to infer the potential drug-target interaction from deep representations. The experiment result shows that a deep neural network with Stacked Auto-Encoders exactly output interactive representations for the DTIs prediction task. MFDR is able to predict large-scale drug-target interactions with high accuracy and achieves results better than other feature-based approaches.
Description: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, Canada, 24-29 July 2016
URI: http://hdl.handle.net/10397/66229
ISBN: 9781509006199
DOI: 10.1109/IJCNN.2016.7727339
Appears in Collections:Conference Paper

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