False positive reduction in automatic segmentation system

Jheyson Vargas, Jairo Andres Velasco, Gloria Ines Alvarez, Diego Luis Linares, Enrique Bravo

Producción: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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An application has been developed for automatic segmentation of Potyvirus polyproteins through stochastic models of Pattern Recognition. These models usually find the correct location of the cleavage site but also suggest other possible locations called false positives. For reducing the number of false positives, we evaluated three methods. The first is to shrink the search range skipping portions of polyprotein with low probability of containing the cleavage site. In the second and third approach, we use a measure to rank candidate locations in order to maximize the ranking of the correct cleavage site. Here we evaluate probability emitted by Hidden Markov Models (HMM) and Minimum Editing Distance (MED) as measure alternatives. Our results indicate that HMM probability is a better quality measure of a candidate location than MED. This probability is useful to eliminate most of false positive. Besides, it allows to quantify the quality of an automatic segmentation.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Biology - Proceedings of the 2nd Colombian Congress on Computational Biology and Bioinformatics CCBCOL 2013
EditorialSpringer Verlag
Número de páginas6
ISBN (versión impresa)9783319015675
EstadoPublicada - 2014
Evento2nd Colombian Congress on Computational Biology and Bioinformatics, CCBCOL 2013 - Manizales, Colombia
Duración: 25 sep. 201327 sep. 2013

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
ISSN (versión impresa)2194-5357


Conferencia2nd Colombian Congress on Computational Biology and Bioinformatics, CCBCOL 2013


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