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Prediction of protein subcellular locations using Markov chain models
Author(s) -
Yuan Zheng
Publication year - 1999
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(99)00506-2
Subject(s) - subcellular localization , markov chain , computer science , protein sequencing , sequence (biology) , protein subcellular localization prediction , computational biology , artificial intelligence , biological system , pattern recognition (psychology) , biology , peptide sequence , biochemistry , machine learning , gene
A novel method was introduced to predict protein subcellular locations from sequences. Using sequence data, this method achieved a prediction accuracy higher than previous methods based on the amino acid composition. For three subcellular locations in a prokaryotic organism, the overall prediction accuracy reached 89.1%. For eukaryotic proteins, prediction accuracies of 73.0% and 78.7% were attained within four and three location categories, respectively. These results demonstrate the applicability of this relative simple method and possible improvement of prediction for the protein subcellular location.