MiRNA-Disease Associations Prediction Based on Negative Sample Selection and Multi-layer Perceptron

Abstract

MicroRNAs (miRNAs) are a class of non-coding RNAs of approximately 22 nucleotides. Cumulative evidence from biological experiments has confirmed that miRNAs play a key role in many complex human diseases. Therefore, the accurate identification of potential associations between miRNAs and diseases is beneficial to understanding the mechanisms of diseases, developing drugs and treating complex diseases. We propose a new method to predict miRNA-disease associations based on a negative sample selection strategy and multi-layer perceptron (called NMLPMDA). For obtaining more similarity information, NMLPMDA integrates the miRNA functional similarity and the Gaussian interaction profile (GIP) kernel similarity of miRNAs as the final miRNA similarity, and integrates the disease semantic similarity and the GIP kernel similarity of diseases as the final disease similarity. In particular, we propose a negative sample selection strategy based on common gene information to select more reliable negative samples from unknown miRNA-disease associations. The 5-fold cross validation is used to evaluate the performance of NMLPMDA and other competing methods. On four datasets (HMDD2.0-Yan, HMDD2.0-Lan, HMDD2.0-You, HMDD3.0), the AUC values of NMLPMDA are 0.9278, 0.9206, 0.9301 and 0.9350, respectively. In addition, we also illustrate the prediction ability of NMLPMDA in Lymphoma. As a result, 28 of the top 30 miRNAs associated with the disease have been validated experimentally in dbDEMC and previous studies, respectively. These experimental results indicate that NMLPMDA is a reliable model for predicting associations between miRNAs and diseases.