LDAP: a web server for lncRNA-disease association prediction.
Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource.



In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity.



The web server is available at http://bioinformatics.csu.edu.cn/ldap



jxwang@mail.csu.edu.cn.



Supplementary data are available at Bioinformatics online.
School of Information Science and Engineering, Central South University, Changsha, China.
Wei
W
Lan
School of Information Science and Engineering, Central South University, Changsha, China.
Min
M
Li
School of Information Science and Engineering, Central South University, Changsha, China.
Kaijie
K
Zhao
School of Information Science and Engineering, Central South University, Changsha, China.
Jin
J
Liu
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.
Fang-Xiang
FX
Wu
Department of Computer Science, Georgia State University, Atlanta, GA, USA.
Yi
Y
Pan
School of Information Science and Engineering, Central South University, Changsha, China.
Jianxin
J
Wang
D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000209:etiology; D006801:Humans; D062085:RNA, Long Noncoding / Q000235:genetics / Q000378:metabolism* / Q000502:physiology; D012984:Software
D062085:RNA, Long Noncoding