Multiple Sequence Alignment using Deep Reinforcement Learning
| dc.contributor.author | Joeres, Roman | |
| dc.contributor.editor | Gesellschaft für Informatik | |
| dc.date.accessioned | 2021-12-15T10:17:11Z | |
| dc.date.available | 2021-12-15T10:17:11Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Multiple sequence alignment (MSA) is one of the primal problems in biology and bioinformatics. The question of how to align multiple sequences correctly is crucial for many other fields of research, e.g., gaining information about the evolutionary distance of two or more sequences and therefore about their corresponding species, finding protein targets for drugs, or finding a drug for a certain target protein. Reinforcement learning (RL), and especially deep reinforcement learning (DRL), has become popular in recent years. To name just a few, DRL has shown major success in complex games such as Atari Games, Chess, and Go. We model the problem of aligning multiple sequences as a Markov decision process (MDP) and examine the performance of different (D)RL algorithms compared to state-of-the-art tools. | en |
| dc.identifier.isbn | 978-3-88579-751-7 | |
| dc.identifier.pissn | 1614-3213 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37785 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | SKILL 2021 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Seminars, Volume S-17 | |
| dc.subject | Bioinformatics | |
| dc.subject | Multiple Sequence Alignment | |
| dc.subject | Reinforcement Learning | |
| dc.subject | Deep Reinforcement Learning | |
| dc.title | Multiple Sequence Alignment using Deep Reinforcement Learning | en |
| gi.citation.endPage | 112 | |
| gi.citation.startPage | 101 | |
| gi.conference.date | 28. September und 01. Oktober 2021 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | SKILL 2021 |
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