Sub-cellular localization prediction is an important step for inferring protein functions. Several strategies have been developed in the recent years to solve this problem, from alignment-based solutions to feature-based solutions. However, under some identity thesholds, these kind of approaches fail to detect homologous sequences, achieving predictions with low specificity and sensitivity. Here, a novel methodology is proposed for classifying proteins with low identity levels. This approach implements a simple, yet powerful assumption that employs hierarchical clustering and hidden Markov models, obtaining high performance on the prediction of four different sub-cellular localizations.
CITATION STYLE
Jaramillo-Garzón, J. A., Castro-Ceballos, J., & Castellanos-Dominguez, G. (2015). Predicting sub-cellular location of proteins based on hierarchical clustering and hidden Markov models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9044, pp. 256–263). Springer Verlag. https://doi.org/10.1007/978-3-319-16480-9_26
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