Prediction and validation of association between microRNAs and diseases by multipath methods

• Published in: Biochimica et biophysica acta Vol. 1860; no. 11; pp. 2735 - 2739
• Main Authors: Zeng, Xiangxiang, Zhang, Xuan, Liao, Yuanlu, Pan, Linqiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2016
• Subjects: Algorithms; artificial intelligence; biomedical research; Computational Biology - methods; Disease - genetics; HeteSim; human diseases; Humans; Link prediction; methodology; microRNA; MicroRNA-disease association prediction; MicroRNAs - genetics; Multipath method; pathogenesis; prediction; ROC Curve; Link prediction; HeteSim; MicroRNA-disease association prediction; Multipath method
• ISSN: 0304-4165; 0006-3002; 1872-8006
• DOI: 10.1016/j.bbagen.2016.03.016

Deciphering the genetic basis of human diseases is an important goal in biomedical research. There is increasing evidence suggesting that microRNAs play critical roles in many key biological processes. So the identification of microRNAs associated with disease is very important for understanding the pathogenesis of diseases. Two multipath methods are introduced to predict the associations between microRNAs and diseases based on microRNA-disease heterogeneous network. The first method, HeteSim_MultiPath (HSMP), uses the HeteSim measure to calculate the similarity between objects and combines the HeteSim scores of different paths with a constant that dampens the contributions of longer paths. The second one, HeteSim_SVM (HSSVM), uses the HeteSim measure and the machine learning method used to combine HeteSim scores instead of a constant. We use the leave-one-out cross-validation to evaluate our novel methods, and find that our methods are better than other methods. We achieve an area under the ROC curve of 0.981 and 0.984 respectively. We also check the top-10 most similarity of microRNAs-diseases associations and find that our predictions are reasonable and credible. The encouraging results suggest that multipath methods can provide help in identifying novel microRNA-disease associations, and guide biological experiments for scientific research. This article is part of a Special Issue entitled “System Genetics”. Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. •HeteSim measure is introduced to predict the associations between microRNAs and diseases.•The performance of prediction is improved by using machine learning method.•The two multipath methods achieve an area under the ROC curve of 0.981 and 0.984, respectively.•The predicted top-10 microRNAs-diseases associations are reasonable and credible.