SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction-Reference-Cited by-同舟云学术

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SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction

Published:2020-01-10 Issue: Volume:7 Page:
ISSN:2296-2646
Container-title:Frontiers in Chemistry
language:
Short-container-title:Front. Chem.

Author:

Zhang Yu-Fang,Wang Xiangeng,Kaushik Aman Chandra,Chu Yanyi,Shan Xiaoqi,Zhao Ming-Zhu,Xu Qin,Wei Dong-Qing

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Frontiers Media SA

Subject

General Chemistry

Reference46 articles.

1. Continuous distributed representation of biological sequences for deep proteomics and genomics;Asgari;PLoS ONE,2015

2. Representation learning: a review and new perspectives;Bengio;IEEE T. Pattern Anal.,2013

3. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect;Cai;J. Cell. Biochem.,2002

4. Drug–target interaction prediction by random walk on the heterogeneous network;Chen;Mol. Biosyst.,2012

5. Convolutional embedding of attributed molecular graphs for physical property prediction;Coley;J. Chem. Inf. Model.,2017

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