207 related articles for article (PubMed ID: 28662047)
1. MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition.
Weng SL; Kao HJ; Huang CH; Lee TY
PLoS One; 2017; 12(6):e0179529. PubMed ID: 28662047
[TBL] [Abstract][Full Text] [Related]
2. Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites.
Huang KY; Kao HJ; Hsu JB; Weng SL; Lee TY
BMC Bioinformatics; 2019 Feb; 19(Suppl 13):384. PubMed ID: 30717647
[TBL] [Abstract][Full Text] [Related]
3. UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.
Huang CH; Su MG; Kao HJ; Jhong JH; Weng SL; Lee TY
BMC Syst Biol; 2016 Jan; 10 Suppl 1(Suppl 1):6. PubMed ID: 26818456
[TBL] [Abstract][Full Text] [Related]
4. MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs.
Bui VM; Lu CT; Ho TT; Lee TY
Bioinformatics; 2016 Jan; 32(2):165-72. PubMed ID: 26411868
[TBL] [Abstract][Full Text] [Related]
5. MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs.
Kao HJ; Weng SL; Huang KY; Kaunang FJ; Hsu JB; Huang CH; Lee TY
BMC Syst Biol; 2017 Dec; 11(Suppl 7):137. PubMed ID: 29322938
[TBL] [Abstract][Full Text] [Related]
6. SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites.
Bui VM; Weng SL; Lu CT; Chang TH; Weng JT; Lee TY
BMC Genomics; 2016 Jan; 17 Suppl 1(Suppl 1):9. PubMed ID: 26819243
[TBL] [Abstract][Full Text] [Related]
7. A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs.
Kao HJ; Huang CH; Bretaña NA; Lu CT; Huang KY; Weng SL; Lee TY
BMC Bioinformatics; 2015; 16 Suppl 18(Suppl 18):S10. PubMed ID: 26680539
[TBL] [Abstract][Full Text] [Related]
8. SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
Lee TY; Chen YJ; Lu TC; Huang HD; Chen YJ
PLoS One; 2011; 6(7):e21849. PubMed ID: 21789187
[TBL] [Abstract][Full Text] [Related]
9. Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features.
Weng SL; Huang KY; Kaunang FJ; Huang CH; Kao HJ; Chang TH; Wang HY; Lu JJ; Lee TY
BMC Bioinformatics; 2017 Mar; 18(Suppl 3):66. PubMed ID: 28361707
[TBL] [Abstract][Full Text] [Related]
10. GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity.
Chen YJ; Lu CT; Huang KY; Wu HY; Chen YJ; Lee TY
PLoS One; 2015; 10(4):e0118752. PubMed ID: 25849935
[TBL] [Abstract][Full Text] [Related]
11. Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.
Huang KY; Hsu JB; Lee TY
Sci Rep; 2019 Nov; 9(1):16175. PubMed ID: 31700141
[TBL] [Abstract][Full Text] [Related]
12. iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features.
Huang KY; Hung FY; Kao HJ; Lau HH; Weng SL
BMC Bioinformatics; 2020 Dec; 21(1):568. PubMed ID: 33297954
[TBL] [Abstract][Full Text] [Related]
13. SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites.
Kao HJ; Nguyen VN; Huang KY; Chang WC; Lee TY
Genomics Proteomics Bioinformatics; 2020 Apr; 18(2):208-219. PubMed ID: 32592791
[TBL] [Abstract][Full Text] [Related]
14. A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.
Li L; Luo Q; Xiao W; Li J; Zhou S; Li Y; Zheng X; Yang H
J Bioinform Comput Biol; 2017 Feb; 15(1):1650025. PubMed ID: 27411307
[TBL] [Abstract][Full Text] [Related]
15. PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity.
Lee TY; Bretaña NA; Lu CT
BMC Bioinformatics; 2011 Jun; 12():261. PubMed ID: 21703007
[TBL] [Abstract][Full Text] [Related]
16. SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.
Hussain W; Khan YD; Rasool N; Khan SA; Chou KC
Anal Biochem; 2019 Mar; 568():14-23. PubMed ID: 30593778
[TBL] [Abstract][Full Text] [Related]
17. The prediction of palmitoylation site locations using a multiple feature extraction method.
Shi SP; Sun XY; Qiu JD; Suo SB; Chen X; Huang SY; Liang RP
J Mol Graph Model; 2013 Mar; 40():125-30. PubMed ID: 23419766
[TBL] [Abstract][Full Text] [Related]
18. Improved prediction of palmitoylation sites using PWMs and SVM.
Li YX; Shao YH; Deng NY
Protein Pept Lett; 2011 Feb; 18(2):186-93. PubMed ID: 21054270
[TBL] [Abstract][Full Text] [Related]
19. A New Scheme to Characterize and Identify Protein Ubiquitination Sites.
Nguyen VN; Huang KY; Huang CH; Lai KR; Lee TY
IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(2):393-403. PubMed ID: 26887002
[TBL] [Abstract][Full Text] [Related]
20. PalmPred: an SVM based palmitoylation prediction method using sequence profile information.
Kumari B; Kumar R; Kumar M
PLoS One; 2014; 9(2):e89246. PubMed ID: 24586628
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]