165 related articles for article (PubMed ID: 33102477)
1. 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]
2. UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.
Huang CH; Su MG; Kao HJ; Jhong JH; Weng SL; Lee TY
BMC Syst Biol; 2016 Jan; 10 Suppl 1(Suppl 1):6. PubMed ID: 26818456
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Mini-review: Recent advances in post-translational modification site prediction based on deep learning.
Meng L; Chan WS; Huang L; Liu L; Chen X; Zhang W; Wang F; Cheng K; Sun H; Wong KC
Comput Struct Biotechnol J; 2022; 20():3522-3532. PubMed ID: 35860402
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
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. Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.
Lin C; Hsu CJ; Lou YS; Yeh SJ; Lee CC; Su SL; Chen HC
J Med Internet Res; 2017 Nov; 19(11):e380. PubMed ID: 29109070
[TBL] [Abstract][Full Text] [Related]
11. UbiNet: an online resource for exploring the functional associations and regulatory networks of protein ubiquitylation.
Nguyen VN; Huang KY; Weng JT; Lai KR; Lee TY
Database (Oxford); 2016; 2016():. PubMed ID: 27114492
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli.
Zulfiqar H; Sun ZJ; Huang QL; Yuan SS; Lv H; Dao FY; Lin H; Li YW
Methods; 2022 Jul; 203():558-563. PubMed ID: 34352373
[TBL] [Abstract][Full Text] [Related]
14. Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.
Le NQK; Ho QT
Methods; 2022 Aug; 204():199-206. PubMed ID: 34915158
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.
Liu CM; Ta VD; Le NQK; Tadesse DA; Shi C
Life (Basel); 2022 Aug; 12(8):. PubMed ID: 36013392
[TBL] [Abstract][Full Text] [Related]
18. Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network.
Khalili E; Ramazi S; Ghanati F; Kouchaki S
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35152280
[TBL] [Abstract][Full Text] [Related]
19. Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation.
Khanal J; Tayara H; Zou Q; Chong KT
Comput Struct Biotechnol J; 2021; 19():1612-1619. PubMed ID: 33868598
[TBL] [Abstract][Full Text] [Related]
20. Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites.
Chen X; Qiu JD; Shi SP; Suo SB; Huang SY; Liang RP
Bioinformatics; 2013 Jul; 29(13):1614-22. PubMed ID: 23626001
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]