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.
Researchers at Central South University, China have developed a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. They have implemented 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 pipeline of LDAP
(A) Calculating similarities of lncRNAs and diseases, respectively. (B) Constructing similarity matrices of lncRNAs and diseases, respectively. (C) Fusing similarity matrices of lncRNAs and diseases in term of Karcher mean, respec- tively. (D) Predicting potential lncRNA-disease associations by using bagging SVM. (E) The output of predicted result.
Availability – The web server is available at http://bioinformatics.csu.edu.cn/ldap