Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm.

摘要: Background MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) extensively adopted these due prediction performance achieved. On other hand, logic decision tree, constructed model is interpretable, less attention.