title: ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes.
abstract: We present a method called ngLOC, an n-gram-based Bayesian classifier that predicts the localization of a protein sequence over ten distinct subcellular organelles. A tenfold cross-validation result shows an accuracy of 89% for sequences localized to a single organelle, and 82% for those localized to multiple organelles. An enhanced version of ngLOC was developed to estimate the subcellular proteomes of eight eukaryotic organisms: yeast, nematode, fruitfly, mosquito, zebrafish, chicken, mouse, and human.
authors.affiliation: Department of Computer Science, State University of New York at Albany, Washington Ave, Albany, New York 12222, USA. bking@cs.albany.edu
authors.forename: Brian R
authors.initials: BR
authors.lastname: King
authors.forename: Chittibabu
authors.initials: C
authors.lastname: Guda
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001499:Bayes Theorem; D002965:Classification; D005057:Eukaryotic Cells / Q000648:ultrastructure*; D006801:Humans; D015388:Organelles / Q000737:chemistry*; D011506:Proteins / Q000032:analysis; D020543:Proteome / Q000032:analysis*; D014018:Tissue Distribution; D015003:Yeasts
chemical_list: D011506:Proteins; D020543:Proteome