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False Positive Reduction in Automatic Segmentation System

  • Jheyson VargasEmail author
  • Jairo Andres Velasco
  • Gloria Ines Alvarez
  • Diego Luis Linares
  • Enrique Bravo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 232)

Abstract

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.

Keywords

Hide Markov Model Cleavage Site Automatic Segmentation Search Range Candidate Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jheyson Vargas
    • 1
    Email author
  • Jairo Andres Velasco
    • 1
  • Gloria Ines Alvarez
    • 1
  • Diego Luis Linares
    • 1
  • Enrique Bravo
    • 1
  1. 1.Pontificia Universidad Javeriana CaliUniversidad del ValleCaliColombia

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