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    STUDIA INFORMATICA - Issue no. 2 / 2014  
         
  Article:   ON REINFORCEMENT LEARNING BASED MULTIPLE SEQUENCE ALIGNMENT.

Authors:  .
 
       
         
  Abstract:   Multiple alignment of biological sequences may reveal important functional, structural or evolutionary relationships between the sequences. Although very important from a biological perspective, the problem of multiple sequence alignment is quite challenging from a computational point of view, as it is NP-complete. Reinforcement learning is an approach to machine intelligence in which an adaptive system can learn to behave in a certain way by receiving punishments or rewards for its chosen actions. In this paper we investigate a reinforcement learning based model for the multiple sequence alignment problem, which combines a Q-learning algorithm with two variations of a sequence alignment algorithm and three di fferent action selection policies. The model is experimentally evaluated on two data sets containing mitochondrial human DNA sequences from remains collected during several archeological excavations. The obtained results for each algorithmic combination are analysed and we provide comparisons of these results.

2010 Mathematics Subject Classi fication. 68P15, 68T05.1998 CR Categories and Descriptors. I.2.6[Computing Methodologies]: Arti cial Intelligence - Learning; I.2.8[Computing Methodologies]: Problem Solving, Control Methods, and Search - Heuristic methods.

Key words and phrases. Bioinformatics, Multiple sequence alignment, Reinforcement learning.
 
         
     
         
         
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