170 related articles for article (PubMed ID: 36897030)
1. ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.
Li Z; Jin J; Wang Y; Long W; Ding Y; Hu H; Wei L
Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36897030
[TBL] [Abstract][Full Text] [Related]
2. An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy.
Wang R; Zhou Z; Wu X; Jiang X; Zhuo L; Liu M; Li H; Fu X; Yao X
J Chem Inf Model; 2024 Apr; 64(7):2798-2806. PubMed ID: 37643082
[TBL] [Abstract][Full Text] [Related]
3. Advances and perspectives in discovery and functional analysis of small secreted proteins in plants.
Hu XL; Lu H; Hassan MM; Zhang J; Yuan G; Abraham PE; Shrestha HK; Villalobos Solis MI; Chen JG; Tschaplinski TJ; Doktycz MJ; Tuskan GA; Cheng ZM; Yang X
Hortic Res; 2021 Jun; 8(1):130. PubMed ID: 34059650
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides.
He W; Wang Y; Cui L; Su R; Wei L
Bioinformatics; 2021 Dec; 37(24):4684-4693. PubMed ID: 34323948
[TBL] [Abstract][Full Text] [Related]
7. AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction.
Liu J; Li M; Chen X
Methods; 2022 Nov; 207():38-43. PubMed ID: 36100141
[TBL] [Abstract][Full Text] [Related]
8. CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.
Li Z; Jin J; He W; Long W; Yu H; Gao X; Nakai K; Zou Q; Wei L
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37861173
[TBL] [Abstract][Full Text] [Related]
9. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.
Yuan Q; Chen K; Yu Y; Le NQK; Chua MCH
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36642410
[TBL] [Abstract][Full Text] [Related]
10. CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only.
Yang X; Jin J; Wang R; Li Z; Wang Y; Wei L
J Chem Inf Model; 2024 Apr; 64(7):2807-2816. PubMed ID: 37252890
[TBL] [Abstract][Full Text] [Related]
11. Accelerating bioactive peptide discovery via mutual information-based meta-learning.
He W; Jiang Y; Jin J; Li Z; Zhao J; Manavalan B; Su R; Gao X; Wei L
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34882225
[TBL] [Abstract][Full Text] [Related]
12. MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.
Liang X; Zhao H; Wang J
Protein Sci; 2024 Apr; 33(4):e4966. PubMed ID: 38532681
[TBL] [Abstract][Full Text] [Related]
13. iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model.
Akbar S; Ahmad A; Hayat M; Rehman AU; Khan S; Ali F
Comput Biol Med; 2021 Oct; 137():104778. PubMed ID: 34481183
[TBL] [Abstract][Full Text] [Related]
14. Anticancer peptides prediction with deep representation learning features.
Lv Z; Cui F; Zou Q; Zhang L; Xu L
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33529337
[TBL] [Abstract][Full Text] [Related]
15. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.
Cui S; Luo Y; Tseng HH; Ten Haken RK; El Naqa I
Med Phys; 2019 May; 46(5):2497-2511. PubMed ID: 30891794
[TBL] [Abstract][Full Text] [Related]
16. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.
Wei L; Ye X; Sakurai T; Mu Z; Wei L
Bioinformatics; 2022 Mar; 38(6):1514-1524. PubMed ID: 34999757
[TBL] [Abstract][Full Text] [Related]
17. HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.
Yaseen A; Gull S; Akhtar N; Amin I; Minhas F
J Bioinform Comput Biol; 2021 Oct; 19(5):2150021. PubMed ID: 34353244
[TBL] [Abstract][Full Text] [Related]
18. Using explainable machine learning to uncover the kinase-substrate interaction landscape.
Zhou Z; Yeung W; Soleymani S; Gravel N; Salcedo M; Li S; Kannan N
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38244571
[TBL] [Abstract][Full Text] [Related]
19. Cross-type biomedical named entity recognition with deep multi-task learning.
Wang X; Zhang Y; Ren X; Zhang Y; Zitnik M; Shang J; Langlotz C; Han J
Bioinformatics; 2019 May; 35(10):1745-1752. PubMed ID: 30307536
[TBL] [Abstract][Full Text] [Related]
20. Identifying multi-functional bioactive peptide functions using multi-label deep learning.
Tang W; Dai R; Yan W; Zhang W; Bin Y; Xia E; Xia J
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34651655
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]