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A Novel Approach Based on Bi-Random Walk to Predict Microbe-Disease Associations

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成果类型:
会议论文
作者:
Shen, Xianjun*;Zhu, Huan;Jiang, Xingpeng;Hu, Xiaohua;Yang, Jincai
通讯作者:
Shen, Xianjun
作者机构:
[Shen, Xianjun; Hu, Xiaohua; Zhu, Huan; Jiang, Xingpeng; Yang, Jincai] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构:
[Shen, Xianjun] C
Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
语种:
英文
关键词:
Microbe-disease associations;Bi-Random Walk;Computational prediction model
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2018
卷:
10956
页码:
746-752
会议名称:
14th International Conference on Intelligent Computing (ICIC)
会议论文集名称:
Lecture Notes in Artificial Intelligence
会议时间:
AUG 15-18, 2018
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Shen, Xianjun;Zhu, Huan;Jiang, Xingpeng;Hu, Xiaohua;Yang, Jincai] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议赞助商:
IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China, Tongji Univ, Wuhan Univ Sci, & Technol, Wuhan Inst Technol
主编:
Huang, DS Gromiha, MM Han, K Hussain, A
出版地:
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
出版者:
SPRINGER INTERNATIONAL PUBLISHING AG
ISBN:
978-3-319-95957-3; 978-3-319-95956-6
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61532008]; National Key Research and Development Program of China [2017YFC0909502]; Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE [CCNU17TS0003]
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:
An increasing number of clinical observations have confirmed that the microbes inhabiting in human body have critical impacts on the progression of human disease, which provides promising insights into understanding the mechanism of diseases. However, the known microbe-disease associations remain limited. So, we proposed Bi-Random Walk based on Multiple Path (BiRWMP) to predict microbe-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation were adopted to demonstrate the capability of proposed method. BiRWMP performed better than other methods. Finally, we lis...

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