Classifier assessment and feature selection for recognizing short coding sequences of human genes.

ABSTRACT: With the ever-increasing pace of genome sequencing, there is a great need for fast and accurate computational tools to automatically identify genes in these genomes. Although great progress has been made in the development of gene-finding algorithms during the past decades, there is still room for further improvement. In particular, the issue of recognizing short exons in eukaryotes is still not solved satisfactorily. This article is devoted to assessing various linear and kernel-based classification algorithms and selecting the best combination of Z-curve features for further improvement of the issue. Eight state-of-the-art linear and kernel-based supervised pattern recognition techniques were used to identify the short (21-192 bp) coding sequences of human genes. By measuring the predict

SUBMITTER: Song K 

PROVIDER: S-EPMC3298678 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature