Using Network Distance Analysis to Predict lncRNA–miRNA Interactions

• Published in: Interdisciplinary sciences : computational life sciences Vol. 13; no. 3; pp. 535 - 545
• Main Authors: Zhang, Li, Yang, Pengyu, Feng, Huawei, Zhao, Qi, Liu, Hongsheng
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Biomarkers; Biomedical and Life Sciences; Case studies; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Computer applications; Health Sciences; Life Sciences; Mathematical and Computational Physics; Medicine; miRNA; Original Research Article; Predictions; Similarity; Source code; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Therapeutic targets; Interaction prediction; miRNA; lncRNA; Network distance
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-021-00458-z

LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA–miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA . Graphic Abstract