180 related articles for article (PubMed ID: 19689425)
21. 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]
22. STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Basith S; Lee G; Manavalan B
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34532736
[TBL] [Abstract][Full Text] [Related]
23. Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features.
Du Y; Zhai Z; Li Y; Lu M; Cai T; Zhou B; Huang L; Wei T; Li T
J Proteome Res; 2016 Dec; 15(12):4234-4244. PubMed ID: 27774790
[TBL] [Abstract][Full Text] [Related]
24. Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine.
Li ZC; Zhou X; Dai Z; Zou XY
Anal Chim Acta; 2011 Oct; 703(2):163-71. PubMed ID: 21889630
[TBL] [Abstract][Full Text] [Related]
25. PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.
Liu Y; Wang M; Xi J; Luo F; Li A
Int J Biol Sci; 2018; 14(8):946-956. PubMed ID: 29989096
[TBL] [Abstract][Full Text] [Related]
26. AutoMotif server: prediction of single residue post-translational modifications in proteins.
Plewczynski D; Tkacz A; Wyrwicz LS; Rychlewski L
Bioinformatics; 2005 May; 21(10):2525-7. PubMed ID: 15728119
[TBL] [Abstract][Full Text] [Related]
27. ASEB: a web server for KAT-specific acetylation site prediction.
Wang L; Du Y; Lu M; Li T
Nucleic Acids Res; 2012 Jul; 40(Web Server issue):W376-9. PubMed ID: 22600735
[TBL] [Abstract][Full Text] [Related]
28. Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.
Ning Q; Yu M; Ji J; Ma Z; Zhao X
BMC Bioinformatics; 2019 Jun; 20(1):346. PubMed ID: 31208321
[TBL] [Abstract][Full Text] [Related]
29. iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features.
Xu Y; Yang Y; Ding J; Li C
IEEE Trans Nanobioscience; 2018 Oct; 17(4):394-401. PubMed ID: 29994125
[TBL] [Abstract][Full Text] [Related]
30. N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites.
Lee TY; Hsu JB; Lin FM; Chang WC; Hsu PC; Huang HD
J Comput Chem; 2010 Nov; 31(15):2759-71. PubMed ID: 20839302
[TBL] [Abstract][Full Text] [Related]
31. Predicting O-glycosylation sites in mammalian proteins by using SVMs.
Li S; Liu B; Zeng R; Cai Y; Li Y
Comput Biol Chem; 2006 Jun; 30(3):203-8. PubMed ID: 16731044
[TBL] [Abstract][Full Text] [Related]
32. PLMD: An updated data resource of protein lysine modifications.
Xu H; Zhou J; Lin S; Deng W; Zhang Y; Xue Y
J Genet Genomics; 2017 May; 44(5):243-250. PubMed ID: 28529077
[TBL] [Abstract][Full Text] [Related]
33. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC.
Ju Z; Cao JZ; Gu H
J Theor Biol; 2016 May; 397():145-50. PubMed ID: 26908349
[TBL] [Abstract][Full Text] [Related]
34. Predicting lysine-malonylation sites of proteins using sequence and predicted structural features.
Taherzadeh G; Yang Y; Xu H; Xue Y; Liew AW; Zhou Y
J Comput Chem; 2018 Aug; 39(22):1757-1763. PubMed ID: 29761520
[TBL] [Abstract][Full Text] [Related]
35. Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation.
Yu K; Zhang Q; Liu Z; Du Y; Gao X; Zhao Q; Cheng H; Li X; Liu ZX
Brief Bioinform; 2020 Sep; 21(5):1798-1805. PubMed ID: 32978618
[TBL] [Abstract][Full Text] [Related]
36. RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences.
Chen L; Chen Y
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38066710
[TBL] [Abstract][Full Text] [Related]
37. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
Anal Biochem; 2016 Mar; 497():48-56. PubMed ID: 26723495
[TBL] [Abstract][Full Text] [Related]
38. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.
Zhang Y; Xie R; Wang J; Leier A; Marquez-Lago TT; Akutsu T; Webb GI; Chou KC; Song J
Brief Bioinform; 2019 Nov; 20(6):2185-2199. PubMed ID: 30351377
[TBL] [Abstract][Full Text] [Related]
39. Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.
Chandra A; Sharma A; Dehzangi A; Shigemizu D; Tsunoda T
BMC Mol Cell Biol; 2019 Dec; 20(Suppl 2):57. PubMed ID: 31856704
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]