NDLI: A Q-learning approach for aligning protein sequences

Content Provider	IEEE Xplore Digital Library
Author	Mircea, I.-G. Czibula, G. Bocicor, M.-I.
Copyright Year	2015
Description	Author affiliation: Fac. of Math. & Comput. Sci., Babes-Bolyai Univ., Cluj-Napoca, Romania (Mircea, I.-G.; Czibula, G.; Bocicor, M.-I.)
Abstract	Protein multiple sequence alignment is significant in the field of bioinformatics as it may reveal important information about the protein sequences' functional, structural or evolutionary relationships. It involves the alignment of three or more biological protein sequences and represents a real challenge both from a biological and a computational point of view. Q-learning is a reinforcement learning technique in which an artificial agent learns to find an optimal sequence of actions to achieve a goal by receiving rewards for its chosen actions. This paper investigates a Q-learning based model for the multiple sequence alignment problem applied on protein sequences. The experimental evaluation of the model is performed on two artificial data sets and on benchmark problem sets selected from the BAliBASE database. The obtained results show the effectiveness of using reinforcement learning for determining the optimal alignment of multiple protein sequences.
Starting Page	51
Ending Page	58
File Size	274974
Page Count	8
File Format	PDF
e-ISBN	9781467382007
DOI	10.1109/ICCP.2015.7312605
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-03
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Training Q-learning Learning (artificial intelligence) Benchmark testing Amino acids Protein Multiple Sequence Alignment Dynamic programming Bioinformatics Machine Learning
Content Type	Text
Resource Type	Article

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