Variational Autoencoder Based Network Embedding Algorithm For Protein Function Prediction-Reference-Cited by-同舟云学术

Variational Autoencoder Based Network Embedding Algorithm For Protein Function Prediction

Published:2022-02-18 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Machine Learning and Computing (ICMLC)
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Author:

CAO GUANSONG¹,ZHU YUAN²,YI MING³

Affiliation:

1. School of Automation, China University of Geosciences, China and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System, China University of Geosciences, Wuhan, China;, China

2. School of Automation, China University of Geosciences, Wuhan, China, China University of Geosciences, China and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System, China University of Geosciences, Wuhan, China;, China

3. School of Mathematics and Physics, China University of Geosciences, Wuhan, China, China University of Geosciences, China

Funder

the Central Universities, China University of Geosciences (Wuhan) Grant CUGGC02,the National Natural Science Foundation of China [11675060 and 91730301 to Yi],the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010,the Major Programs of National Statistical Science Research Foundation under Grant 2018LD02

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3529836.3529922

Reference17 articles.

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3. Graph2GO: a multi-modal attributed network embedding method for inferring protein functions

4. STRING v9.1: protein-protein interaction networks, with increased coverage and integration

5. Peng J , Xue H , Wei Z , . 2021. Integrating multi-network topology for gene function prediction using deep neural networks. Briefings in bioinformatics 22, 2(March 2021 ), 2096-2105. https://doi.org/10.1093/bib/bbaa036 Peng J, Xue H, Wei Z, . 2021. Integrating multi-network topology for gene function prediction using deep neural networks. Briefings in bioinformatics 22, 2(March 2021), 2096-2105. https://doi.org/10.1093/bib/bbaa036