Evaluating multiple sequence alignments using a LS-SVM approach with a heterogeneous set of biological features

Authors:
Francisco Ortuño;Olga Valenzuela;Héctor Pomares;Ignacio Rojas
Affiliations:
Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, Spain;Department of Applied Mathematics, University of Granada, Spain;Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, Spain;Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

Citing 5
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Abstract

Multiple sequence alignment (MSA) is an essential approach to apply in other outstanding bioinformatics tasks such as structural predictions, biological function analyses or phylogenetic modeling. However, current MSA methodologies do not reach a consensus about how sequences must be accurately aligned. Moreover, these tools usually provide partially optimal alignments, as each one is focused on specific features. Thus, the same set of sequences can provide quite different alignments, overall when sequences are less related. Consequently, researchers and biologists do not agree on how the quality of MSAs should be evaluated in order to decide the most adequate methodology. Therefore, recent evaluations tend to use more complex scores including supplementary biological features. In this work, we address the evaluation of MSAs by using a novel supervised learning approach based on Least Square Support Vector Machine (LS-SVM). This algorithm will include a set of heterogeneous features and scores in order to determine the alignment accuracies. It is assessed by means of the benchmark BAliBASE.