title: Discrimination of outer membrane proteins using a K-nearest neighbor method.
abstract: Identification of outer membrane proteins (OMPs) from genome is an important task. This paper presents a k-nearest neighbor (K-NN) method for discriminating outer membrane proteins (OMPs). The method makes predictions based on a weighted Euclidean distance that is computed from residue composition. The method achieves 89.1% accuracy with 0.668 MCC (Matthews correlation coefficient) in discriminating OMPs and non-OMPs. The performance of the method is improved by including homologous information into the calculation of residue composition. The final method achieves an accuracy of 96.1%, with 0.873 MCC, 87.5% sensitivity, and 98.2% specificity. Comparisons with multiple recently published methods show that the method proposed in this study outperforms the others.
authors.affiliation: Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. charles.yan@usu.edu
authors.forename: C
authors.initials: C
authors.lastname: Yan
authors.forename: J
authors.initials: J
authors.lastname: Hu
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000818:Animals; D001425:Bacterial Outer Membrane Proteins / Q000235:genetics*; D006090:Gram-Negative Bacteria / Q000235:genetics*; D008957:Models, Genetic; D011237:Predictive Value of Tests; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D001425:Bacterial Outer Membrane Proteins