237 related articles for article (PubMed ID: 23117653)
1. Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.
Gao YF; Li BQ; Cai YD; Feng KY; Li ZD; Jiang Y
Mol Biosyst; 2013 Jan; 9(1):61-9. PubMed ID: 23117653
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches.
Li BQ; Hu LL; Niu S; Cai YD; Chou KC
J Proteomics; 2012 Feb; 75(5):1654-65. PubMed ID: 22178444
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS.
Zhang J; Lyu Y; Ma Z
Dis Markers; 2022; 2022():5892627. PubMed ID: 36246558
[TBL] [Abstract][Full Text] [Related]
7. Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis.
Zheng LL; Niu S; Hao P; Feng K; Cai YD; Li Y
PLoS One; 2011; 6(12):e28221. PubMed ID: 22174779
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Prediction of protein cleavage site with feature selection by random forest.
Li BQ; Cai YD; Feng KY; Zhao GJ
PLoS One; 2012; 7(9):e45854. PubMed ID: 23029276
[TBL] [Abstract][Full Text] [Related]
10. Prediction of lysine ubiquitination with mRMR feature selection and analysis.
Cai Y; Huang T; Hu L; Shi X; Xie L; Li Y
Amino Acids; 2012 Apr; 42(4):1387-95. PubMed ID: 21267749
[TBL] [Abstract][Full Text] [Related]
11. PINGU: PredIction of eNzyme catalytic residues usinG seqUence information.
Pai PP; Ranjani SS; Mondal S
PLoS One; 2015; 10(8):e0135122. PubMed ID: 26261982
[TBL] [Abstract][Full Text] [Related]
12. Predicting DNA-binding sites of proteins based on sequential and 3D structural information.
Li BQ; Feng KY; Ding J; Cai YD
Mol Genet Genomics; 2014 Jun; 289(3):489-99. PubMed ID: 24448651
[TBL] [Abstract][Full Text] [Related]
13. DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.
Ma X; Guo J; Sun X
PLoS One; 2016; 11(12):e0167345. PubMed ID: 27907159
[TBL] [Abstract][Full Text] [Related]
14. Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods.
Liu Y; Gu W; Zhang W; Wang J
Biomed Res Int; 2015; 2015():561547. PubMed ID: 25961025
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Position-specific analysis and prediction of protein pupylation sites based on multiple features.
Zhao X; Dai J; Ning Q; Ma Z; Yin M; Sun P
Biomed Res Int; 2013; 2013():109549. PubMed ID: 24066285
[TBL] [Abstract][Full Text] [Related]
17. A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.
Zhou Y; Zhang N; Li BQ; Huang T; Cai YD; Kong XY
J Biomol Struct Dyn; 2015; 33(11):2479-90. PubMed ID: 25616595
[TBL] [Abstract][Full Text] [Related]
18. Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.
Petrova NV; Wu CH
BMC Bioinformatics; 2006 Jun; 7():312. PubMed ID: 16790052
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Computational prediction and analysis of protein γ-carboxylation sites based on a random forest method.
Zhang N; Li BQ; Gao S; Ruan JS; Cai YD
Mol Biosyst; 2012 Nov; 8(11):2946-55. PubMed ID: 22918520
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]