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.
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 <firstname.lastname@example.org>
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)
Loading required package: pracma Loading required package: Matrix Attaching package: 'Matrix' The following objects are masked from 'package:pracma': expm, lu, tril, triu  "bsxfun.se" "dot.ext" "getTargetScores" "initialization"  "logmvgamma" "logsumexp" "sort_components" "targetScore"  "vbgmm" "vbound" "vexp" "vmax"
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.