RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule–MiRNA Associations

More and more studies found that
many complex human diseases occur
accompanied by aberrant expression of microRNAs (miRNAs). Small molecule
(SM) drugs have been utilized to treat complex human diseases by affecting
the expression of miRNAs. Several computational methods were proposed
to infer underlying associations between SMs and miRNAs. In our study,
we proposed a new calculation model of random forest based small molecule–miRNA
association prediction (RFSMMA) which was based on the known SM–miRNA
associations in the SM2miR database. RFSMMA utilized the similarity
of SMs and miRNAs as features to represent SM–miRNA pairs and
further implemented the machine learning algorithm of random forest
to train training samples and obtain a prediction model. In RFSMMA,
integrating multiple kinds of similarity can avoid the bias of single
similarity and choosing more reliable features from original features
can represent SM–miRNA pairs more accurately. We carried out
cross validations to assess predictive accuracy of RFSMMA. As a result,
RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ±
0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed
local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively,
under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456,
0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations
according to the order mentioned above. In addition, we implemented
a case study on three common SMs, namely, 5-fluorouracil, 17β-estradiol,
and 5-aza-2′-deoxycytidine. Among the top 50 associated miRNAs
of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were
verified, respectively. Therefore, RFSMMA is shown to be an effective
and reliable tool for identifying underlying SM–miRNA associations.