Bioinformatics Advance Access originally published online on October 6, 2009
Bioinformatics 2009 25(23):3084-3092; doi:10.1093/bioinformatics/btp567
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Adaptive multi-agent architecture for functional sequence motifs recognition
1 Department of Computer Science and 2 Departments of Pathology & Laboratory Medicine, Oncology, Biochemistry & Molecular Biology and Medical Genetics, University of Calgary, Calgary, AB, Canada
* To whom correspondence should be addressed.
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Abstract |
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Motivation: Accurate genome annotation or protein function prediction requires precise recognition of functional sequence motifs. Many computational motif prediction models have been proposed. Due to the complexity of the biological data, it may be desirable to apply an integrated approach that uses multiple models for analysis.
Results: In this article, we propose a novel multi-agent architecture for the general purpose of functional sequence motif recognition. The approach takes advantage of the synergy provided by multiple agents through the employment of different agents equipped with distinctive problem solving skills and promotes the collaborations among them through decision maker (DM) agents that work as classifier ensembles. A genetic algorithm-based fusion strategy is applied which offers evolutionary property to the DM agents. The consistency and robustness of the system are maintained by an evolvable agent that mediates the team of the ensemble agents. The combined effort of a recommendation system (Seer) and the self-learning mediator agent yields a successful identification of the most efficient agent deployment scheme at an early stage of the experimentation process, which has the potential of greatly reducing the computational cost of the system. Two concrete systems are constructed that aim at predicting two important sequence motifs—the translational initiation sites (TISs) and the core promoters. With the incorporation of three distinctive problem solver agents, the TIS predictor consistently outperforms most of the state-of-the-art approaches under investigation. Integrating three existing promoter predictors, our system is able to yield consistently good performance.
Availability: The program (MotifMAS) and the datasets are available upon request.
Contact: jzeng{at}ucalgary.ca
Associate Editor: Limsoon Wong
Received on February 26, 2009; revised on September 26, 2009; accepted on September 28, 2009
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