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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jia Zeng Alhajj, R. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB (Jia Zeng; Alhajj, R.) |
| Abstract | The accurate recognition of translation initiation sites (TISs) is an important stage in genome annotation. Due to the complicated nature of the genetic information and our incomplete understanding of it, TIS prediction remains a challenging undertaking. Many computational approaches have been proposed in the literature, some of which have yielded quite impressive performance. However, most of them either investigate the genomic sequences from one single perspective or apply some static central fusion mechanism on a fixed set of features. In this paper, we extend our previous work which proposed a novel multi-agent architecture for TIS prediction and explore the application of reinforcement learning into the negotiation process. Experimental results on three benchmark data sets have shown the effectiveness and robustness of incorporating reinforcement learning in the system. |
| Starting Page | 241 |
| Ending Page | 248 |
| File Size | 635337 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424417780 |
| DOI | 10.1109/CIBCB.2008.4675786 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning Genomics Bioinformatics Support vector machines Support vector machine classification Proteins Organisms Predictive models Computer science Sequences genomic sequences multi-agent system reinforcement learning TIS prediction |
| Content Type | Text |
| Resource Type | Article |
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