281 related articles for article (PubMed ID: 25752770)
1. Prediction of O-glycosylation sites based on multi-scale composition of amino acids and feature selection.
Chen Y; Zhou W; Wang H; Yuan Z
Med Biol Eng Comput; 2015 Jun; 53(6):535-44. PubMed ID: 25752770
[TBL] [Abstract][Full Text] [Related]
2. Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.
Cai B; Jiang X
BMC Bioinformatics; 2016 Mar; 17():116. PubMed ID: 26940649
[TBL] [Abstract][Full Text] [Related]
3. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.
Ju Z; He JJ
J Mol Graph Model; 2017 Sep; 76():356-363. PubMed ID: 28763688
[TBL] [Abstract][Full Text] [Related]
4. Sequence-based prediction of DNA-binding residues in proteins with conservation and correlation information.
Ma X; Guo J; Liu HD; Xie JM; Sun X
IEEE/ACM Trans Comput Biol Bioinform; 2012; 9(6):1766-75. PubMed ID: 22868682
[TBL] [Abstract][Full Text] [Related]
5. Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.
Zhao X; Zhao X; Bao L; Zhang Y; Dai J; Yin M
Molecules; 2017 Nov; 22(11):. PubMed ID: 29099805
[TBL] [Abstract][Full Text] [Related]
6. Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm.
Ju Z; Wang SY
J Theor Biol; 2018 Nov; 457():6-13. PubMed ID: 30125576
[TBL] [Abstract][Full Text] [Related]
7. SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.
Al-Barakati HJ; McConnell EW; Hicks LM; Poole LB; Newman RH; Kc DB
Sci Rep; 2018 Jul; 8(1):11288. PubMed ID: 30050050
[TBL] [Abstract][Full Text] [Related]
8. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC.
Xie HL; Fu L; Nie XD
Protein Eng Des Sel; 2013 Nov; 26(11):735-42. PubMed ID: 24048266
[TBL] [Abstract][Full Text] [Related]
9. Prediction of protein N-formylation using the composition of k-spaced amino acid pairs.
Ju Z; Cao JZ
Anal Biochem; 2017 Oct; 534():40-45. PubMed ID: 28709899
[TBL] [Abstract][Full Text] [Related]
10. Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs.
Chen YZ; Tang YR; Sheng ZY; Zhang Z
BMC Bioinformatics; 2008 Feb; 9():101. PubMed ID: 18282281
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids.
Jia CZ; He WY; Yao YH
J Biomol Struct Dyn; 2017 Mar; 35(4):829-835. PubMed ID: 26957000
[TBL] [Abstract][Full Text] [Related]
13. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.
Taherzadeh G; Campbell M; Zhou Y
Methods Mol Biol; 2022; 2499():177-186. PubMed ID: 35696081
[TBL] [Abstract][Full Text] [Related]
14. Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information.
Ma X; Guo J; Sun X
J Bioinform Comput Biol; 2018 Jun; 16(3):1840009. PubMed ID: 29591488
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection.
Zeng Y; Yuan Z; Chen Y; Hu Y
J Bioinform Comput Biol; 2023 Oct; 21(5):2350024. PubMed ID: 37899352
[TBL] [Abstract][Full Text] [Related]
18. iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.
Ju Z; Cao JZ; Gu H
J Theor Biol; 2015 Nov; 385():50-7. PubMed ID: 26254214
[TBL] [Abstract][Full Text] [Related]
19. Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.
Ju Z; He JJ
Anal Biochem; 2018 Jun; 550():1-7. PubMed ID: 29641975
[TBL] [Abstract][Full Text] [Related]
20. A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.
Yang R; Zhang C; Gao R; Zhang L
Int J Mol Sci; 2016 Feb; 17(2):218. PubMed ID: 26861308
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
