These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

271 related articles for article (PubMed ID: 33316035)

  • 1. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.
    Liang X; Li F; Chen J; Li J; Wu H; Li S; Song J; Liu Q
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33316035
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.
    Manavalan B; Basith S; Lee G
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34595489
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder.
    Ghafoor H; Asim MN; Ibrahim MA; Ahmed S; Dengel A
    Comput Biol Med; 2024 Jun; 176():108538. PubMed ID: 38759585
    [TBL] [Abstract][Full Text] [Related]  

  • 5. AntiCP 2.0: an updated model for predicting anticancer peptides.
    Agrawal P; Bhagat D; Mahalwal M; Sharma N; Raghava GPS
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32770192
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer.
    Azad H; Akbar MY; Sarfraz J; Haider W; Riaz MN; Ali GM; Ghazanfar S
    J Biomol Struct Dyn; 2024 Mar; ():1-14. PubMed ID: 38450672
    [TBL] [Abstract][Full Text] [Related]  

  • 8. PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.
    Arif M; Musleh S; Fida H; Alam T
    Sci Rep; 2024 Jul; 14(1):16992. PubMed ID: 39043738
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.
    Charoenkwan P; Chiangjong W; Lee VS; Nantasenamat C; Hasan MM; Shoombuatong W
    Sci Rep; 2021 Feb; 11(1):3017. PubMed ID: 33542286
    [TBL] [Abstract][Full Text] [Related]  

  • 10. ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies.
    Zhong G; Deng L
    J Chem Inf Model; 2024 Feb; 64(3):1092-1104. PubMed ID: 38277774
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.
    Wang X; Chai Z; Li S; Liu Y; Li C; Jiang Y; Liu Q
    Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317054
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides.
    Wan Y; Wang Z; Lee TY
    BMC Bioinformatics; 2021 May; 22(1):286. PubMed ID: 34051755
    [TBL] [Abstract][Full Text] [Related]  

  • 13. ACPS: An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data.
    Kaushik AC; Li M; Mehmood A; Dai X; Wei DQ
    Chem Biol Drug Des; 2021 Feb; 97(2):372-382. PubMed ID: 32916036
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.
    Zhao Y; Wang S; Fei W; Feng Y; Shen L; Yang X; Wang M; Wu M
    Int J Mol Sci; 2021 May; 22(11):. PubMed ID: 34073203
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides.
    Basith S; Manavalan B; Shin TH; Lee DY; Lee G
    Curr Protein Pept Sci; 2020; 21(12):1242-1250. PubMed ID: 31957610
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Contrastive learning for enhancing feature extraction in anticancer peptides.
    Lee B; Shin D
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38725157
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins.
    Charoenkwan P; Schaduangrat N; Hasan MM; Moni MA; Lió P; Shoombuatong W
    EXCLI J; 2022; 21():554-570. PubMed ID: 35651661
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DRACP: a novel method for identification of anticancer peptides.
    Zhao T; Hu Y; Zang T
    BMC Bioinformatics; 2020 Dec; 21(Suppl 16):559. PubMed ID: 33323099
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Effective identification and differential analysis of anticancer peptides.
    Zhang L; Hu X; Xiao K; Kong L
    Biosystems; 2024 Jul; 241():105246. PubMed ID: 38848816
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ACP-ML: A sequence-based method for anticancer peptide prediction.
    Bian J; Liu X; Dong G; Hou C; Huang S; Zhang D
    Comput Biol Med; 2024 Mar; 170():108063. PubMed ID: 38301519
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.