283 related articles for article (PubMed ID: 31782357)
1. Prediction of Citrullination Sites on the Basis of mRMR Method and SNN.
Liu M; Liu G
Comb Chem High Throughput Screen; 2019; 22(10):705-715. PubMed ID: 31782357
[TBL] [Abstract][Full Text] [Related]
2. Predicting Citrullination Sites in Protein Sequences Using mRMR Method and Random Forest Algorithm.
Zhang Q; Sun X; Feng K; Wang S; Zhang YH; Wang S; Lu L; Cai YD
Comb Chem High Throughput Screen; 2017; 20(2):164-173. PubMed ID: 28029071
[TBL] [Abstract][Full Text] [Related]
3. Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.
Wang S; Zhang YH; Huang G; Chen L; Cai YD
Comb Chem High Throughput Screen; 2017; 20(2):96-106. PubMed ID: 28000567
[TBL] [Abstract][Full Text] [Related]
4. Protein Arginine Deiminases (PADs): Biochemistry and Chemical Biology of Protein Citrullination.
Mondal S; Thompson PR
Acc Chem Res; 2019 Mar; 52(3):818-832. PubMed ID: 30844238
[TBL] [Abstract][Full Text] [Related]
5. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition.
Ju Z; Wang SY
Gene; 2018 Jul; 664():78-83. PubMed ID: 29694908
[TBL] [Abstract][Full Text] [Related]
6. Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods.
Chen L; Wang S; Zhang YH; Wei L; Xu X; Huang T; Cai YD
Comb Chem High Throughput Screen; 2018; 21(6):393-402. PubMed ID: 29848272
[TBL] [Abstract][Full Text] [Related]
7. Prediction of tyrosine sulfation with mRMR feature selection and analysis.
Niu S; Huang T; Feng K; Cai Y; Li Y
J Proteome Res; 2010 Dec; 9(12):6490-7. PubMed ID: 20973568
[TBL] [Abstract][Full Text] [Related]
8. Characterization of Citrullination Sites in Neutrophils and Mast Cells Activated by Ionomycin via Integration of Mass Spectrometry and Machine Learning.
Chaerkady R; Zhou Y; Delmar JA; Weng SHS; Wang J; Awasthi S; Sims D; Bowen MA; Yu W; Cazares LH; Sims GP; Hess S
J Proteome Res; 2021 Jun; 20(6):3150-3164. PubMed ID: 34008986
[TBL] [Abstract][Full Text] [Related]
9. Chemical biology of protein citrullination by the protein A arginine deiminases.
Mondal S; Thompson PR
Curr Opin Chem Biol; 2021 Aug; 63():19-27. PubMed ID: 33676233
[TBL] [Abstract][Full Text] [Related]
10. Citrullination regulates the expression of insulin-like growth factor-binding protein 1 (IGFBP1) in ovine uterine luminal epithelial cells.
Young CH; Rothfuss HM; Gard PF; Muth A; Thompson PR; Ashley RL; Cherrington BD
Reproduction; 2017 Jan; 153(1):1-10. PubMed ID: 29565015
[TBL] [Abstract][Full Text] [Related]
11. Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.
Li BQ; Feng KY; Chen L; Huang T; Cai YD
PLoS One; 2012; 7(8):e43927. PubMed ID: 22937126
[TBL] [Abstract][Full Text] [Related]
12. Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.
Gao J; Tao XW; Zhao J; Feng YM; Cai YD; Zhang N
Comb Chem High Throughput Screen; 2017; 20(7):629-637. PubMed ID: 28292250
[TBL] [Abstract][Full Text] [Related]
13. UbNiRF: A Hybrid Framework Based on Null Importances and Random Forest that Combines Multiple Features to Predict Ubiquitination Sites in
Li X; Yuan Z; Chen Y
Front Biosci (Landmark Ed); 2024 May; 29(5):197. PubMed ID: 38812315
[TBL] [Abstract][Full Text] [Related]
14. A Streamlined Data Analysis Pipeline for the Identification of Sites of Citrullination.
Maurais AJ; Salinger AJ; Tobin M; Shaffer SA; Weerapana E; Thompson PR
Biochemistry; 2021 Sep; 60(38):2902-2914. PubMed ID: 34491035
[TBL] [Abstract][Full Text] [Related]
15. Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.
Ma X; Guo J; Sun X
Biomed Res Int; 2015; 2015():425810. PubMed ID: 26543860
[TBL] [Abstract][Full Text] [Related]
16. The Role of Citrullination Modification in CD4
Chen Y; Teng Y; Xu P; Wang S
Biomolecules; 2024 Mar; 14(4):. PubMed ID: 38672418
[TBL] [Abstract][Full Text] [Related]
17. A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.
Ni Q; Chen L
Comb Chem High Throughput Screen; 2017; 20(7):612-621. PubMed ID: 28292249
[TBL] [Abstract][Full Text] [Related]
18. Citrullination of myofilament proteins in heart failure.
Fert-Bober J; Giles JT; Holewinski RJ; Kirk JA; Uhrigshardt H; Crowgey EL; Andrade F; Bingham CO; Park JK; Halushka MK; Kass DA; Bathon JM; Van Eyk JE
Cardiovasc Res; 2015 Nov; 108(2):232-42. PubMed ID: 26113265
[TBL] [Abstract][Full Text] [Related]
19. A novel method of predicting protein disordered regions based on sequence features.
Zhao TH; Jiang M; Huang T; Li BQ; Zhang N; Li HP; Cai YD
Biomed Res Int; 2013; 2013():414327. PubMed ID: 23710446
[TBL] [Abstract][Full Text] [Related]
20. An in silico analysis of primary and secondary structure specificity determinants for human peptidylarginine deiminase types 2 and 4.
Olson JS; Lubner JM; Meyer DJ; Grant JE
Comput Biol Chem; 2017 Oct; 70():107-115. PubMed ID: 28850877
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
