title: Predictability of protein subcellular locations by pattern recognition techniques.
abstract: An analysis of the predictability of subcellular locations is performed by using simple pattern recognition techniques in an attempt to capture the real dimensions of the problem at hand. Results show that there are some particular locations that does not need of high complexity classification models to be predicted with high accuracies, and some partial biological explanations are formulated. All the experiments were carried out over a set of Arabidopsis Thaliana proteins and classes were defined according to the plants GO slim.
authors.affiliation: Deperatamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, km 7 vía al Magdalena, (Caldas), Colombia. jajaramillog@unal.edu.co
authors.forename: J A
authors.initials: JA
authors.lastname: Jaramillo-Garzon
authors.forename: A
authors.initials: A
authors.lastname: Perera-Lluna
authors.forename: C G
authors.initials: CG
authors.lastname: Castellanos-Dominguez
mesh_terms: D000595:Amino Acid Sequence; D029681:Arabidopsis Proteins / Q000737:chemistry / Q000145:classification / Q000378:metabolism*; D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods*; D021381:Protein Transport; D013347:Subcellular Fractions / Q000378:metabolism
chemical_list: D029681:Arabidopsis Proteins