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 *

125 related articles for article (PubMed ID: 38277774)

  • 1. 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]  

  • 2. 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]  

  • 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. ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation.
    Chen XG; Zhang W; Yang X; Li C; Chen H
    Front Genet; 2021; 12():698477. PubMed ID: 34276801
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.
    Huang KY; Tseng YJ; Kao HJ; Chen CH; Yang HH; Weng SL
    Sci Rep; 2021 Jun; 11(1):13594. PubMed ID: 34193950
    [TBL] [Abstract][Full Text] [Related]  

  • 6. mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.
    Boopathi V; Subramaniyam S; Malik A; Lee G; Manavalan B; Yang DC
    Int J Mol Sci; 2019 Apr; 20(8):. PubMed ID: 31013619
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.
    Rao B; Zhou C; Zhang G; Su R; Wei L
    Brief Bioinform; 2020 Sep; 21(5):1846-1855. PubMed ID: 31729528
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.
    Cao J; Zhou W; Yu Q; Ji J; Zhang J; He S; Zhu Z
    IEEE J Biomed Health Inform; 2023 Dec; PP():. PubMed ID: 38147420
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.
    Zhong G; Liu H; Deng L
    Interdiscip Sci; 2024 Jul; ():. PubMed ID: 38972032
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.
    Ahmed S; Muhammod R; Khan ZH; Adilina S; Sharma A; Shatabda S; Dehzangi A
    Sci Rep; 2021 Dec; 11(1):23676. PubMed ID: 34880291
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides.
    Schaduangrat N; Nantasenamat C; Prachayasittikul V; Shoombuatong W
    Molecules; 2019 May; 24(10):. PubMed ID: 31121946
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.
    Alsanea M; Dukyil AS; Afnan ; Riaz B; Alebeisat F; Islam M; Habib S
    Sensors (Basel); 2022 May; 22(11):. PubMed ID: 35684624
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm.
    Yu L; Jing R; Liu F; Luo J; Li Y
    Mol Ther Nucleic Acids; 2020 Dec; 22():862-870. PubMed ID: 33230481
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.