413 related articles for article (PubMed ID: 34002774)
1. 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]
2. Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method.
Lv H; Dao FY; Guan ZX; Yang H; Li YW; Lin H
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33099604
[TBL] [Abstract][Full Text] [Related]
3. CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins.
Khanal J; Kandel J; Tayara H; Chong KT
Comput Struct Biotechnol J; 2023; 21():120-127. PubMed ID: 36544479
[TBL] [Abstract][Full Text] [Related]
4. iKcr_CNN: A novel computational tool for imbalance classification of human nonhistone crotonylation sites based on convolutional neural networks with focal loss.
Dou L; Zhang Z; Xu L; Zou Q
Comput Struct Biotechnol J; 2022; 20():3268-3279. PubMed ID: 35832615
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network.
Khanal J; Tayara H; Zou Q; To Chong K
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34882222
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks.
Tng SS; Le NQK; Yeh HY; Chua MCH
J Proteome Res; 2022 Jan; 21(1):265-273. PubMed ID: 34812044
[TBL] [Abstract][Full Text] [Related]
9. PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants.
Jiang Y; Yan R; Wang X
Plant Methods; 2024 Feb; 20(1):28. PubMed ID: 38360730
[TBL] [Abstract][Full Text] [Related]
10. Using ATCLSTM-Kcr to predict and generate the human lysine crotonylation database.
Yang YH; Wu SF; Kong J; Zhu YP; Liu JF; Yang JT
J Proteomics; 2023 Jun; 281():104905. PubMed ID: 37059219
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.
Qiao Y; Zhu X; Gong H
Bioinformatics; 2022 Jan; 38(3):648-654. PubMed ID: 34643684
[TBL] [Abstract][Full Text] [Related]
13. Large-scale lysine crotonylation analysis reveals its potential role in spermiogenesis in the Chinese mitten crab Eriocheir sinensis.
Bao C; Song C; Liu Y; Yang Y; Cui Z
J Proteomics; 2020 Aug; 226():103891. PubMed ID: 32629196
[TBL] [Abstract][Full Text] [Related]
14. Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.
Chen Z; Zhao P; Li F; Wang Y; Smith AI; Webb GI; Akutsu T; Baggag A; Bensmail H; Song J
Brief Bioinform; 2020 Sep; 21(5):1676-1696. PubMed ID: 31714956
[TBL] [Abstract][Full Text] [Related]
15. ARES-Kcr: A New Network Model Utilizing Attention Mechanism and Residual Structure for the Prediction of Lysine Crotonylation Sites.
Tong L; Yong S; Li W; Yang X; Wang X
Stud Health Technol Inform; 2023 Nov; 308():505-512. PubMed ID: 38007777
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Ultradeep Lysine Crotonylome Reveals the Crotonylation Enhancement on Both Histones and Nonhistone Proteins by SAHA Treatment.
Wu Q; Li W; Wang C; Fan P; Cao L; Wu Z; Wang F
J Proteome Res; 2017 Oct; 16(10):3664-3671. PubMed ID: 28882038
[TBL] [Abstract][Full Text] [Related]
18. Protein Crotonylation Expert Review: A New Lens to Take Post-Translational Modifications and Cell Biology to New Heights.
Subba P; Prasad TSK
OMICS; 2021 Oct; 25(10):617-625. PubMed ID: 34582706
[TBL] [Abstract][Full Text] [Related]
19. Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.
Wang R; Wang Z; Wang H; Pang Y; Lee TY
Sci Rep; 2020 Nov; 10(1):20447. PubMed ID: 33235255
[TBL] [Abstract][Full Text] [Related]
20. Global Involvement of Lysine Crotonylation in Protein Modification and Transcription Regulation in Rice.
Liu S; Xue C; Fang Y; Chen G; Peng X; Zhou Y; Chen C; Liu G; Gu M; Wang K; Zhang W; Wu Y; Gong Z
Mol Cell Proteomics; 2018 Oct; 17(10):1922-1936. PubMed ID: 30021883
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
