Improving gene prediction accuracy by combining gene-finding programs based on reinforcement learning
碩士 === 銘傳大學 === 資訊工程學系碩士班 === 92 === A great number of gene-finding programs have been developed for annotating newly sequenced DNA genomes. However, none of them have consistent performance over various species. Recently, a decision fusion concept that improves the prediction accuracy by combining...
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| Format: | Others |
| Language: | en_US |
| Online Access: | http://ndltd.ncl.edu.tw/handle/vp327f |
| Summary: | 碩士 === 銘傳大學 === 資訊工程學系碩士班 === 92 === A great number of gene-finding programs have been developed for annotating newly sequenced DNA genomes. However, none of them have consistent performance over various species. Recently, a decision fusion concept that improves the prediction accuracy by combining the predictions obtained by multiple gene-finding programs has been raised. The existing combination methods are relatively ad-hoc or lack intensive experiments. In this paper, we propose a new combination method based on reinforcement learning which learns from history predictions obtained by existing gene-finding programs and derives the optimal policy for selecting the best prediction program at each nucleotide. The experimental results manifest that the proposed method can significantly improves the performance compared to the single best program.
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