TY - GEN
T1 - False positive reduction in automatic segmentation system
AU - Vargas, Jheyson
AU - Velasco, Jairo Andres
AU - Alvarez, Gloria Ines
AU - Linares, Diego Luis
AU - Bravo, Enrique
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84894834978&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-01568-2_15
DO - 10.1007/978-3-319-01568-2_15
M3 - Conference contribution
AN - SCOPUS:84894834978
SN - 9783319015675
T3 - Advances in Intelligent Systems and Computing
SP - 103
EP - 108
BT - Advances in Computational Biology - Proceedings of the 2nd Colombian Congress on Computational Biology and Bioinformatics CCBCOL 2013
PB - Springer Verlag
T2 - 2nd Colombian Congress on Computational Biology and Bioinformatics, CCBCOL 2013
Y2 - 25 September 2013 through 27 September 2013
ER -