title: Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution.
abstract: In the Post Genome Age, there is an urgent need to develop the reliable and effective computational methods to predict the subcellular localization for the explosion of newly found proteins. Here, a novel method of pseudo amino acid (PseAA) composition, the so-called "amino acid composition distribution" (AACD), is introduced. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented by a feature vector. Finally, the feature vectors of all sequences thus obtained are further input into the multi-class support vector machines to predict the subcellular localization. The results show that AACD is quite effective in representing protein sequences for the purpose of predicting protein subcellular localization.
authors.affiliation: School of Computer Science and Engneering, Northwestern Polytechnical University, Xi'an, China.
authors.forename: J-Y
authors.initials: JY
authors.lastname: Shi
authors.forename: S-W
authors.initials: SW
authors.lastname: Zhang
authors.forename: Q
authors.initials: Q
authors.lastname: Pan
authors.forename: G-P
authors.initials: GP
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis*; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D042541:Intracellular Space / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000032:analysis; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D010455:Peptides; D011506:Proteins