266 related articles for article (PubMed ID: 38245002)
1. Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.
Ramazi S; Tabatabaei SAH; Khalili E; Nia AG; Motarjem K
Database (Oxford); 2024 Jan; 2024():. PubMed ID: 38245002
[TBL] [Abstract][Full Text] [Related]
2. A hybrid feature extraction scheme for efficient malonylation site prediction.
Sorkhi AG; Pirgazi J; Ghasemi V
Sci Rep; 2022 Apr; 12(1):5756. PubMed ID: 35388017
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.
Chen Z; He N; Huang Y; Qin WT; Liu X; Li L
Genomics Proteomics Bioinformatics; 2018 Dec; 16(6):451-459. PubMed ID: 30639696
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins.
Chung CR; Chang YP; Hsu YL; Chen S; Wu LC; Horng JT; Lee TY
Sci Rep; 2020 Jun; 10(1):10541. PubMed ID: 32601280
[TBL] [Abstract][Full Text] [Related]
7. Prediction of Lysine Malonylation Sites Based on Pseudo Amino Acid.
Xiang Q; Feng K; Liao B; Liu Y; Huang G
Comb Chem High Throughput Screen; 2017; 20(7):622-628. PubMed ID: 28292251
[TBL] [Abstract][Full Text] [Related]
8. RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.
Al-Barakati H; Thapa N; Hiroto S; Roy K; Newman RH; Kc D
Comput Struct Biotechnol J; 2020; 18():852-860. PubMed ID: 32322367
[TBL] [Abstract][Full Text] [Related]
9. A deep learning method to more accurately recall known lysine acetylation sites.
Wu M; Yang Y; Wang H; Xu Y
BMC Bioinformatics; 2019 Jan; 20(1):49. PubMed ID: 30674277
[TBL] [Abstract][Full Text] [Related]
10. ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.
Jia X; Zhao P; Li F; Qin Z; Ren H; Li J; Miao C; Zhao Q; Akutsu T; Dou G; Chen Z; Song J
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36880172
[TBL] [Abstract][Full Text] [Related]
11. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.
Chen Z; Liu X; Li F; Li C; Marquez-Lago T; Leier A; Akutsu T; Webb GI; Xu D; Smith AI; Li L; Chou KC; Song J
Brief Bioinform; 2019 Nov; 20(6):2267-2290. PubMed ID: 30285084
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.
Chen YZ; Wang ZZ; Wang Y; Ying G; Chen Z; Song J
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34002774
[TBL] [Abstract][Full Text] [Related]
14. Computational prediction of species-specific malonylation sites via enhanced characteristic strategy.
Wang LN; Shi SP; Xu HD; Wen PP; Qiu JD
Bioinformatics; 2017 May; 33(10):1457-1463. PubMed ID: 28025199
[TBL] [Abstract][Full Text] [Related]
15. Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm.
Wang S; Li J; Sun X; Zhang YH; Huang T; Cai Y
Comb Chem High Throughput Screen; 2020; 23(4):304-312. PubMed ID: 30588879
[TBL] [Abstract][Full Text] [Related]
16. DeepNphos: A deep-learning architecture for prediction of N-phosphorylation sites.
Chang X; Zhu Y; Chen Y; Li L
Comput Biol Med; 2024 Mar; 170():108079. PubMed ID: 38295472
[TBL] [Abstract][Full Text] [Related]
17. Malonylome analysis in developing rice (Oryza sativa) seeds suggesting that protein lysine malonylation is well-conserved and overlaps with acetylation and succinylation substantially.
Mujahid H; Meng X; Xing S; Peng X; Wang C; Peng Z
J Proteomics; 2018 Jan; 170():88-98. PubMed ID: 28882676
[TBL] [Abstract][Full Text] [Related]
18. Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.
Li Z; Fang J; Wang S; Zhang L; Chen Y; Pian C
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35189635
[TBL] [Abstract][Full Text] [Related]
19. Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.
Liu X; Xu LL; Lu YP; Yang T; Gu XY; Wang L; Liu Y
Front Genet; 2022; 13():1007618. PubMed ID: 36246655
[TBL] [Abstract][Full Text] [Related]
20. KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine β-Hydroxybutyrylation (Kbhb) modification sites.
Chen L; Liu L; Su H; Xu Y
Methods; 2024 Jul; 227():27-34. PubMed ID: 38679187
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]