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.
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Ortuño, F., Valenzuela, O., Pomares, H., Rojas, I. (2013). Evaluating Multiple Sequence Alignments Using a LS-SVM Approach with a Heterogeneous Set of Biological Features. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_18
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DOI: https://doi.org/10.1007/978-3-642-38682-4_18
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