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Predicting Gene-Disease Associations with Manifold Learning

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成果类型:
期刊论文、会议论文
作者:
Luo, Ping;Tian, Li-Ping;Chen, Bolin;Xiao, Qianghua;Wu, Fang-Xiang*
通讯作者:
Wu, Fang-Xiang
作者机构:
[Luo, Ping; Wu, Fang-Xiang] Univ Saskatchewan, Div Biomed Engn, Sakatoon, SK, Canada.
[Tian, Li-Ping] Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China.
[Chen, Bolin] Northwestern Polytech Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China.
[Xiao, Qianghua] Univ South China, Sch Math & Phys, Hengyang, Peoples R China.
[Wu, Fang-Xiang] Nankai Univ, Sch Math Sci, Tianjin, Peoples R China.
通讯机构:
[Wu, Fang-Xiang] U
[Wu, Fang-Xiang] N
Univ Saskatchewan, Div Biomed Engn, Sakatoon, SK, Canada.
Nankai Univ, Sch Math Sci, Tianjin, Peoples R China.
Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK, Canada.
语种:
英文
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2018
卷:
10847
页码:
265-271
会议名称:
14th International Symposium on Bioinformatics Research and Applications (ISBRA)
会议论文集名称:
Lecture Notes in Bioinformatics
会议时间:
JUN 08-11, 2018
会议地点:
Beijing, PEOPLES R CHINA
会议主办单位:
[Luo, Ping;Wu, Fang-Xiang] Univ Saskatchewan, Div Biomed Engn, Sakatoon, SK, Canada.^[Tian, Li-Ping] Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China.^[Chen, Bolin] Northwestern Polytech Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China.^[Xiao, Qianghua] Univ South China, Sch Math & Phys, Hengyang, Peoples R China.^[Wu, Fang-Xiang] Nankai Univ, Sch Math Sci, Tianjin, Peoples R China.^[Wu, Fang-Xiang] Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK, Canada.
主编:
Zhang, F Cai, Z Skums, P Zhang, S
出版地:
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
出版者:
SPRINGER INTERNATIONAL PUBLISHING AG
ISBN:
978-3-319-94968-0; 978-3-319-94967-3
基金类别:
Natural Science and Engineering Research Council of Canada (NSERC), China Scholarship Council (CSC); National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61772552, 61571052]
机构署名:
本校为其他机构
院系归属:
数理学院
摘要:
In this study, we propose a manifold learning-based method for predicting disease genes by assuming that a disease and its associated genes should be consistent in some lower dimensional manifold. The 10-fold cross-validation experiments show that the area under of the receiver operating characteristic (ROC) curve (AUC) generated by our approach is 0.7452 with high-quality gene-disease associations in OMIM dataset, which is greater that of the competing method PBCF (0.5700). 9 out of top 10 predicted gene-disease associations can be supported by existing literature, which is better than the re...

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