192 related articles for article (PubMed ID: 30218638)
1. Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm.
Ju Z; Wang SY
Anal Biochem; 2018 Nov; 561-562():11-17. PubMed ID: 30218638
[TBL] [Abstract][Full Text] [Related]
2. Predicting lysine glycation sites using bi-profile bayes feature extraction.
Ju Z; Sun J; Li Y; Wang L
Comput Biol Chem; 2017 Dec; 71():98-103. PubMed ID: 29040908
[TBL] [Abstract][Full Text] [Related]
3. Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction
Ju Z; Wang SY
Curr Genomics; 2019 Dec; 20(8):592-601. PubMed ID: 32581647
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.
Ju Z; Wang SY
Anal Biochem; 2023 Feb; 663():115032. PubMed ID: 36592921
[TBL] [Abstract][Full Text] [Related]
7. Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components.
Ju Z; Wang SY
Genomics; 2020 Jan; 112(1):859-866. PubMed ID: 31175975
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. 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]
12. Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling.
Jia C; Zhang M; Fan C; Li F; Song J
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):1937-1945. PubMed ID: 31804942
[TBL] [Abstract][Full Text] [Related]
13. LipoSVM: Prediction of Lysine Lipoylation in Proteins based on the Support Vector Machine.
Wu M; Lu P; Yang Y; Liu L; Wang H; Xu Y; Chu J
Curr Genomics; 2019 Aug; 20(5):362-370. PubMed ID: 32476993
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique.
Jia C; Zuo Y; Zou Q
Bioinformatics; 2018 Jun; 34(12):2029-2036. PubMed ID: 29420699
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques.
Sohrawordi M; Hossain MA
Biochimie; 2022 Jan; 192():125-135. PubMed ID: 34627982
[TBL] [Abstract][Full Text] [Related]
19. Identify and analysis crotonylation sites in histone by using support vector machines.
Qiu WR; Sun BQ; Tang H; Huang J; Lin H
Artif Intell Med; 2017 Nov; 83():75-81. PubMed ID: 28283358
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]