Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.
MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.
School of Information Science and Technology, Xiamen University, Xiamen 361005, China ; School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
Quan
Q
Zou
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Jinjin
J
Li
Software School, Xiamen University, Xiamen 361005, China.
Qingqi
Q
Hong
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Ziyu
Z
Lin
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
Yun
Y
Wu
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
Hua
H
Shi
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Ying
Y
Ju
D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D000069550:Machine Learning; D035683:MicroRNAs / Q000235:genetics*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods; D015398:Signal Transduction / Q000235:genetics*; D060756:Social Networking
D035683:MicroRNAs