Title :
Reinforcement Learning Method for BioAgents
Author :
Ralha, Celia Ghedini ; Schneider, Hugo Wruck ; Walter, Maria Emlia Machado Telles ; Bazzan, Ana Lucia Cetertich
Author_Institution :
Depto. de Cienc. da Comput., Univ. de Brasilia, Brasilia, Brazil
Abstract :
Machine Learning (ML) techniques are being employed in bioinformatics with increasing success. However, two problems are still prohibitive for symbolic ML methods: huge amount of data and lack of examples for training purposes. Thus, this paper introduces the use of reinforcement learning (RL), with the objective of dealing with these two drawbacks. Our work proposes and implement a RL method for the Bio Agents system, in order to improve the annotation of biological sequences in genome sequencing projects. Experiments were done with real data from two different genome sequencing projects: Paracoccidioides brasiliensis - Pb fungus and Paullinia cupana - Guaraná plant. To assign reinforcement signals we have used reference genomes with curated annotations that are considered correct, these signals tackle specific databases and alignment algorithms. The results obtained with the inclusion of a RL layer in Bio Agents were better compared with the system without the proposed method. Also, to the best of our knowledge, this is the first attempt to apply RL techniques to annotation in bioinformatics projects.
Keywords :
bioinformatics; genomics; learning (artificial intelligence); BioAgents; Guarana plant; Paracoccidioides brasiliensis; Paullinia cupana; bioinformatics; biological sequence; genome sequencing project; machine learning technique; reinforcement learning method; symbolic ML method; Bioinformatics; Databases; Genomics; Lead; Manuals; Protocols; BioAgents; bioinformatics; machine learning; reinforcement learning; reinforcement learning method;
Conference_Titel :
Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on
Conference_Location :
Sao Paulo
Print_ISBN :
978-1-4244-8391-4
Electronic_ISBN :
1522-4899
DOI :
10.1109/SBRN.2010.27