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Multiple Sequence Alignment using Deep Reinforcement Learning

dc.contributor.authorJoeres, Roman
dc.contributor.editorGesellschaft für Informatik
dc.date.accessioned2021-12-15T10:17:11Z
dc.date.available2021-12-15T10:17:11Z
dc.date.issued2021
dc.description.abstractMultiple 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.isbn978-3-88579-751-7
dc.identifier.pissn1614-3213
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37785
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofSKILL 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Seminars, Volume S-17
dc.subjectBioinformatics
dc.subjectMultiple Sequence Alignment
dc.subjectReinforcement Learning
dc.subjectDeep Reinforcement Learning
dc.titleMultiple Sequence Alignment using Deep Reinforcement Learningen
gi.citation.endPage112
gi.citation.startPage101
gi.conference.date28. September und 01. Oktober 2021
gi.conference.locationBerlin
gi.conference.sessiontitleSKILL 2021

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