We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Reinforcement Learning for Improving Gene Identification Accuracy by Combination of Gene-Finding Programs.
- Authors
Peng-Yeng Yin; Shyong Jian Shyu; Shih-Ren Yang; Yu-Chung Chang
- Abstract
Due to the explosive and growing size of the genome database, the discovery of gene has become one of the most computationally intensive tasks in bioinformatics. Many such systems have been developed to find genes; however, there is still some room to improve the prediction accuracy. This paper proposes a reinforcement learning model for a combination of gene predictions from existing gene-finding programs. The model learns the optimal policy for accepting the best predictions. The fitness of a policy is reinforced if the selected prediction at a nucleotide site correctly corresponds to the true annotation. The model searches for the optimal policy which maximizes the expected prediction accuracy over all nucleotide sites in the sequences. The experimental results demonstrate that the proposed model yields higher prediction accuracy than that obtained by the single best program.
- Publication
International Journal of Applied Metaheuristic Computing, 2012, Vol 3, Issue 1, p34
- ISSN
1947-8283
- Publication type
Article
- DOI
10.4018/jamc.2012010104