Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 14, Issue 6, Pages 1468-1475Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2599866
Keywords
Collective prediction; miRNA; disease; transduction learning; miRNA-disease network
Categories
Funding
- National Natural Science Foundation of China [61572180]
- Hunan Provincial Natural Science Foundation of China [13JJ2017, 2015JJ2032]
Ask authors/readers for more resources
The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available