Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

• Published in: Journal of computer science and technology Vol. 36; no. 2; pp. 276 - 287
• Main Authors: Dai, Ling-Yun, Liu, Jin-Xing, Zhu, Rong, Wang, Juan, Yuan, Sha-Sha
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.04.2021; Springer; Springer Nature B.V; School of Computer Science,Qufu Normal University,Rizhao 276826,China
• Subjects: Algorithms; Artificial Intelligence; Case studies; Computational linguistics; Computer Science; Data Structures and Information Theory; Differentiation (biology); Disease; Information Systems Applications (incl.Internet); Kernels; Language processing; Medical research; Medicine, Experimental; MicroRNA; MicroRNAs; Natural language interfaces; Random walk; Regular Paper; Similarity; Software Engineering; Theory of Computation; nearest known neighbour; miRNA-disease association; logistic function; weighted; bi-random walk; Gaussian interaction profile; weighted K-nearest known neigh-bour
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-021-0740-2

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K -nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.