An effective multi-task learning framework for drug repurposing based on graph representation learning

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Ye, Shengwei;Zhao, Weizhong;Shen, Xianjun;Jiang, Xingpeng;He, Tingting

通讯作者：

Zhao, WZ

作者机构：

[Shen, Xianjun; Zhao, Weizhong; Zhao, WZ; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.

[Shen, Xianjun; Zhao, Weizhong; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.

[Shen, Xianjun; Zhao, Weizhong; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Hubei, Peoples R China.

通讯机构：

[Zhao, WZ ] C

Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.

语种：

英文

关键词：

Article;controlled study;convolutional neural network;disease association;drug development;drug repositioning;feature learning (machine learning);human;learning;prediction;drug development;Drug Development;Drug Discovery;Drug Repositioning

期刊：

Methods

ISSN：

1046-2023

年：

2023

卷：

218

页码：

48-56

DOI：

10.1016/j.ymeth.2023.07.008

基金类别：

The work is supported by the National Natural Science Foundation of China (No. 61932008 ), the Natural Science Foundation of Hubei Province of China (No. 2022CFB289 ), the Fundamental Research Funds for Central Universities ( KJ02502022-0450 ), and the Scientific Research Center Program of National Language Commission ( ZDI135-135 ). Authors are grateful to the anonymous reviewers for helpful comments.

机构署名：

本校为第一且通讯机构

院系归属：

计算机学院

摘要：

Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we pr...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

An effective multi-task learning framework for drug repurposing based on graph representation learning

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问