156 related articles for article (PubMed ID: 36293242)
1. PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.
Wang H; Wang J; Feng Z; Li Y; Zhao H
Int J Mol Sci; 2022 Oct; 23(20):. PubMed ID: 36293242
[TBL] [Abstract][Full Text] [Related]
2. Deep2Pep: A deep learning method in multi-label classification of bioactive peptide.
Chen L; Hu Z; Rong Y; Lou B
Comput Biol Chem; 2024 Apr; 109():108021. PubMed ID: 38308955
[TBL] [Abstract][Full Text] [Related]
3. IUP-BERT: Identification of Umami Peptides Based on BERT Features.
Jiang L; Jiang J; Wang X; Zhang Y; Zheng B; Liu S; Zhang Y; Liu C; Wan Y; Xiang D; Lv Z
Foods; 2022 Nov; 11(22):. PubMed ID: 36429332
[TBL] [Abstract][Full Text] [Related]
4. Predicting protein-peptide binding residues via interpretable deep learning.
Wang R; Jin J; Zou Q; Nakai K; Wei L
Bioinformatics; 2022 Jun; 38(13):3351-3360. PubMed ID: 35604077
[TBL] [Abstract][Full Text] [Related]
5. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features.
Zhuang J; Gao W; Su R
J Bioinform Comput Biol; 2024 Feb; 22(1):2450001. PubMed ID: 38406833
[TBL] [Abstract][Full Text] [Related]
6. Umami-BERT: An interpretable BERT-based model for umami peptides prediction.
Zhang J; Yan W; Zhang Q; Li Z; Liang L; Zuo M; Zhang Y
Food Res Int; 2023 Oct; 172():113142. PubMed ID: 37689906
[TBL] [Abstract][Full Text] [Related]
7. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
Charoenkwan P; Nantasenamat C; Hasan MM; Manavalan B; Shoombuatong W
Bioinformatics; 2021 Sep; 37(17):2556-2562. PubMed ID: 33638635
[TBL] [Abstract][Full Text] [Related]
8. DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.
Yang J; Cheng Z; Gong F; Fu Y
Anal Chem; 2023 Apr; 95(15):6235-6243. PubMed ID: 36908083
[TBL] [Abstract][Full Text] [Related]
9. Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.
Liu H; Zhang Z; Xu Y; Wang N; Huang Y; Yang Z; Jiang R; Chen H
J Med Internet Res; 2021 Jan; 23(1):e19689. PubMed ID: 33433395
[TBL] [Abstract][Full Text] [Related]
10. AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.
Lee H; Lee S; Lee I; Nam H
Protein Sci; 2023 Jan; 32(1):e4529. PubMed ID: 36461699
[TBL] [Abstract][Full Text] [Related]
11. Extracting comprehensive clinical information for breast cancer using deep learning methods.
Zhang X; Zhang Y; Zhang Q; Ren Y; Qiu T; Ma J; Sun Q
Int J Med Inform; 2019 Dec; 132():103985. PubMed ID: 31627032
[TBL] [Abstract][Full Text] [Related]
12. BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information.
Zhang L; Qin X; Liu M; Liu G; Ren Y
Comput Math Methods Med; 2021; 2021():7764764. PubMed ID: 34484416
[TBL] [Abstract][Full Text] [Related]
13. iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.
Yu Y; He W; Jin J; Xiao G; Cui L; Zeng R; Wei L
Bioinformatics; 2021 Dec; 37(24):4603-4610. PubMed ID: 34601568
[TBL] [Abstract][Full Text] [Related]
14. A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.
Le NQK; Ho QT; Nguyen TT; Ou YY
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33539511
[TBL] [Abstract][Full Text] [Related]
15. Advancing Drug-Target Interaction prediction with BERT and subsequence embedding.
Yang Z; Liu J; Yang F; Zhang X; Zhang Q; Zhu X; Jiang P
Comput Biol Chem; 2024 Jun; 110():108058. PubMed ID: 38593480
[TBL] [Abstract][Full Text] [Related]
16. PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.
Cao X; He W; Chen Z; Li Y; Wang K; Zhang H; Wei L; Cui L; Su R; Wei L
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34117740
[TBL] [Abstract][Full Text] [Related]
17. BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for
Liu Y; Liu Y; Wang GA; Cheng Y; Bi S; Zhu X
Front Bioinform; 2022; 2():834153. PubMed ID: 36304324
[TBL] [Abstract][Full Text] [Related]
18. DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.
Son J; Na S; Paek E
Anal Chem; 2023 Aug; 95(30):11193-11200. PubMed ID: 37459568
[TBL] [Abstract][Full Text] [Related]
19. DeepMSPeptide: peptide detectability prediction using deep learning.
Serrano G; Guruceaga E; Segura V
Bioinformatics; 2020 Feb; 36(4):1279-1280. PubMed ID: 31529040
[TBL] [Abstract][Full Text] [Related]
20. PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only.
Cheng H; Rao B; Liu L; Cui L; Xiao G; Su R; Wei L
Anal Chem; 2021 Apr; 93(16):6481-6490. PubMed ID: 33843206
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
