Monte-Carlo Tree Search for the Multiple Sequence Alignment Problem
The paper considers solving the multiple sequence alignment, a combinatorial challenge in computational biology, where several DNA RNA, or protein sequences are to be arranged for high similarity. The proposal applies randomized Monte-Carlo tree search with nested rollouts and is able to improve the...
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| Published in | Proceedings of the International Symposium on Combinatorial Search Vol. 6; no. 1; pp. 9 - 17 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.09.2021
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| Online Access | Get full text |
| ISSN | 2832-9171 2832-9163 2832-9163 |
| DOI | 10.1609/socs.v6i1.18359 |
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| Summary: | The paper considers solving the multiple sequence alignment, a combinatorial challenge in computational biology, where several DNA RNA, or protein sequences are to be arranged for high similarity. The proposal applies randomized Monte-Carlo tree search with nested rollouts and is able to improve the solution quality over time. Instead of learning the position of the letters, the approach learns a policy for the position of the gaps. The Monte-Carlo beam search algorithm we have implemented has a low memory overhead and can be invoked with constructed or known initial solutions. Experiments in the BAliBASE benchmark show promising results in improving state-of-the-art alignments. |
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| ISSN: | 2832-9171 2832-9163 2832-9163 |
| DOI: | 10.1609/socs.v6i1.18359 |