TargetScore-package: TargetScore: Infer microRNA targets using...

Description Details Author(s) References See Also Examples


Infer the posterior distributions of microRNA targets by probabilistically modeling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variational Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.


Package: TargetScore
Type: Package
Version: 1.1.5
Date: 2013-10-15
License: GPL-2

The front-end main function targetScore should be used to obtain the probablistic score of miRNA target. The workhourse function is vbgmm, which implementates multivariate variational Bayesian Gaussian mixture model.


Yue Li <>


Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S., and Johnson, J. M. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 433(7027), 769-773.

Bartel, D. P. (2009). MicroRNAs: Target Recognition and Regulatory Functions. Cell, 136(2), 215-233.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Information Science and Statistics. NY, USA. (p474-486)

See Also




Example output

Loading required package: pracma
Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:pracma':

    expm, lu, tril, triu

 [1] ""       "dot.ext"         "getTargetScores" "initialization" 
 [5] "logmvgamma"      "logsumexp"       "sort_components" "targetScore"    
 [9] "vbgmm"           "vbound"          "vexp"            "vmax"           

TargetScore documentation built on Nov. 8, 2020, 6:56 p.m.