Open AccessSNMDA : A novel method for predicting micro RNA ‐disease associations based on sparse neighbourhood
Author(s) -
Qu Yu,
Zhang Huaxiang,
Liang Cheng,
Ding Pingjian,
Luo Jiawei
Publication year - 2018
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.13799
Subject(s) - rna , neighbourhood (mathematics) , computational biology , disease , similarity (geometry) , non coding rna , computer science , artificial intelligence , biology , medicine , mathematics , genetics , gene , pathology , mathematical analysis , image (mathematics)
mi RNA s are a class of small noncoding RNA s that are associated with a variety of complex biological processes. Increasing studies have shown that mi RNA s have close relationships with many human diseases. The prediction of the associations between mi RNA s and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time‐consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease‐related mi RNA s based on computational methods. In this study, we present a novel approach to predict the potential micro RNA ‐disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the mi RNA ‐disease association network and integrates the sparse information into the current similarity matrices for both mi RNA s and diseases. To demonstrate the utility of our method, we applied global LOOCV , local LOOCV and five‐fold cross‐validation to evaluate our method, respectively. The corresponding AUC s are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown mi RNA ‐disease associations. Overall, results show that SNMDA can predict the potential associations between mi RNA s and diseases effectively.