116 related articles for article (PubMed ID: 37702240)
1. Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.
Ahmed FF; Podder A; Bulbul MF; Hossain MA; Hasan M; Sarkar MAR; Kim D
Comb Chem High Throughput Screen; 2024; 27(9):1381-1393. PubMed ID: 37702240
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties.
Chen Z; Zhou Y; Song J; Zhang Z
Biochim Biophys Acta; 2013 Aug; 1834(8):1461-7. PubMed ID: 23603789
[TBL] [Abstract][Full Text] [Related]
5. Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.
Tasmia SA; Kibria MK; Tuly KF; Islam MA; Khatun MS; Hasan MM; Mollah MNH
Sci Rep; 2022 Feb; 12(1):2632. PubMed ID: 35173235
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs.
Chen Z; Chen YZ; Wang XF; Wang C; Yan RX; Zhang Z
PLoS One; 2011; 6(7):e22930. PubMed ID: 21829559
[TBL] [Abstract][Full Text] [Related]
8. Improved Prediction of Protein-Protein Interaction Mapping on
Islam MM; Alam MJ; Ahmed FF; Hasan MM; Mollah MNH
Protein Pept Lett; 2021; 28(1):74-83. PubMed ID: 32520672
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.
Nilamyani AN; Auliah FN; Moni MA; Shoombuatong W; Hasan MM; Kurata H
Int J Mol Sci; 2021 Mar; 22(5):. PubMed ID: 33800121
[TBL] [Abstract][Full Text] [Related]
11. Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.
Zhao X; Zhang W; Xu X; Ma Z; Yin M
PLoS One; 2012; 7(10):e46302. PubMed ID: 23110047
[TBL] [Abstract][Full Text] [Related]
12. An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on
Tasmia SA; Ahmed FF; Mosharaf P; Hasan M; Mollah NH
Curr Genomics; 2021 Feb; 22(2):122-136. PubMed ID: 34220299
[TBL] [Abstract][Full Text] [Related]
13. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC.
Ju Z; He JJ
J Mol Graph Model; 2017 Oct; 77():200-204. PubMed ID: 28886434
[TBL] [Abstract][Full Text] [Related]
14. Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration : Malonylation site prediction.
Liu X; Wang L; Li J; Hu J; Zhang X
BMC Genomics; 2020 Nov; 21(1):812. PubMed ID: 33225896
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. 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]
19. predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue.
Hasan MA; Li J; Ahmad S; Molla MK
Anal Biochem; 2017 May; 525():107-113. PubMed ID: 28286168
[TBL] [Abstract][Full Text] [Related]
20. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.
Zhao X; Ning Q; Chai H; Ma Z
J Theor Biol; 2015 Jun; 374():60-5. PubMed ID: 25843215
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
