Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction

• Published in: PeerJ (San Francisco, CA) CA), 2023-08, Vol.11, p.e15889, Article e15889
• Main Authors: Qu, Jia, Song, Zihao, Cheng, Xiaolong, Jiang, Zhibin, Zhou, Jie
• Format: Article
• Language: English
• Subjects: Association prediction; Bioinformatics; Case studies; Computational Biology; Gene expression; Genes; Homeopathy; Materia medica and therapeutics; MicroRNA; Neighborhood-based inference; Neural networks; Restricted Boltzmann Machine; Small molecule; Therapeutics
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.15889

Background A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation. Methods This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations. Results Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ±0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ±0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ±0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.