A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues.
Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the p...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 9 |
|---|---|
| Autores principales: | , , , |
| Formato: | computer program equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/10/2016
|
| 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=113631275&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113631275 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/10/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113631275 113631275 113631275 10.1155/2016/8151509 113631275 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues. aug: au: Guo, Song Liu, Chunhua Zhou, Peng Li, Yanling affil: School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China sug: subj: Tyrosine Sulfates Biochemical Phenomena Proteins Analysis Descriptive Statistics Data Analysis Software Algorithms ROC Curve Funding Source ab: Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields. pubtype: Academic Journal doctype: computer program equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|