title: A Gram-Negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile.
abstract: Prediction of secreted protein types based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 6800 features are extracted at 17 different gaps; then, 309 features are selected by a filter feature selection method based on the training set. To verify the performance of our method, jackknife and independent dataset tests are performed on the test set and the reported overall accuracies are 93.60% and 100%, respectively. Comparison of our results with the existing method shows that our method provides the favorable performance for secreted protein type prediction.
authors: Department of Sciences, Dalian Nationalities University, Dalian 116600, China.
authors: Shuyan
authors: 0000-0002-4677-0436
authors: S
authors: Ding
authors: School of Mathematics and Statistics, Xidian University, Xi'an 710071, China.
authors: Shengli
authors: 0000-0001-8786-0940
authors: S
authors: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism*; D058947:Bacterial Secretion Systems / Q000737:chemistry / Q000378:metabolism*; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D006090:Gram-Negative Bacteria / Q000378:metabolism*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D056510:Position-Specific Scoring Matrices; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D001426:Bacterial Proteins; D058947:Bacterial Secretion Systems