A Gram-Negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile.
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 differe...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 6 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/2/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117146325&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117146325 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/2/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117146325 117146325 117146325 10.1155/2016/3206741 117146325 ppf: 1 ppct: 5 formats: fmt: @attributes: type: P tig: atl: A Gram-Negative Bacterial Secreted Protein Types Prediction Method Based on PSI-BLAST Profile. aug: au: Ding, Shuyan Zhang, Shengli affil: Department of Sciences, Dalian Nationalities University, Dalian 116600, China sug: subj: Proteins Biological Markers Gram-Negative Bacterial Infections Secretions Human Factor Analysis Algorithms Sensitivity and Specificity Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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