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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 …