151 related articles for article (PubMed ID: 34903012)
1. UPFPSR: a ubiquitylation predictor for plant through combining sequence information and random forest.
Yin S; Zheng J; Jia C; Zou Q; Lin Z; Shi H
Math Biosci Eng; 2022 Jan; 19(1):775-791. PubMed ID: 34903012
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. Incorporating Deep Learning With Word Embedding to Identify Plant Ubiquitylation Sites.
Wang H; Wang Z; Li Z; Lee TY
Front Cell Dev Biol; 2020; 8():572195. PubMed ID: 33102477
[TBL] [Abstract][Full Text] [Related]
6. UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites.
Siraj A; Lim DY; Tayara H; Chong KT
Genes (Basel); 2021 May; 12(5):. PubMed ID: 34064731
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.
Fu H; Yang Y; Wang X; Wang H; Xu Y
BMC Bioinformatics; 2019 Feb; 20(1):86. PubMed ID: 30777029
[TBL] [Abstract][Full Text] [Related]
9. EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction.
Chandra AA; Sharma A; Dehzangi A; Tsunoda T
BMC Genomics; 2019 Apr; 19(Suppl 9):984. PubMed ID: 30999859
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Computational identification of ubiquitylation sites from protein sequences.
Tung CW; Ho SY
BMC Bioinformatics; 2008 Jul; 9():310. PubMed ID: 18625080
[TBL] [Abstract][Full Text] [Related]
14. Detecting Succinylation sites from protein sequences using ensemble support vector machine.
Ning Q; Zhao X; Bao L; Ma Z; Zhao X
BMC Bioinformatics; 2018 Jun; 19(1):237. PubMed ID: 29940836
[TBL] [Abstract][Full Text] [Related]
15. Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture.
He F; Wang R; Li J; Bao L; Xu D; Zhao X
BMC Syst Biol; 2018 Nov; 12(Suppl 6):109. PubMed ID: 30463553
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions.
Naseer S; Ali RF; Khan YD; Dominic PDD
J Biomol Struct Dyn; 2022; 40(22):11691-11704. PubMed ID: 34396935
[TBL] [Abstract][Full Text] [Related]
18. Prediction of lysine ubiquitylation with ensemble classifier and feature selection.
Zhao X; Li X; Ma Z; Yin M
Int J Mol Sci; 2011; 12(12):8347-61. PubMed ID: 22272076
[TBL] [Abstract][Full Text] [Related]
19. An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.
He F; Li J; Wang R; Zhao X; Han Y
BMC Bioinformatics; 2021 Oct; 22(1):519. PubMed ID: 34689734
[TBL] [Abstract][Full Text] [Related]
20. A systematic identification of species-specific protein succinylation sites using joint element features information.
Hasan MM; Khatun MS; Mollah MNH; Yong C; Guo D
Int J Nanomedicine; 2017; 12():6303-6315. PubMed ID: 28894368
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
