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 *

226 related articles for article (PubMed ID: 34051755)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 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. 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]  

  • 8. 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]  

  • 9. Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery.
    Wu Q; Ke H; Li D; Wang Q; Fang J; Zhou J
    Curr Top Med Chem; 2019; 19(1):4-16. PubMed ID: 30674262
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Recent Advances in Computational Methods for Identifying Anticancer Peptides.
    Feng P; Wang Z
    Curr Drug Targets; 2019; 20(5):481-487. PubMed ID: 30068270
    [TBL] [Abstract][Full Text] [Related]  

  • 12. mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.
    Sangaraju VK; Pham NT; Wei L; Yu X; Manavalan B
    J Mol Biol; 2024 Sep; 436(17):168687. PubMed ID: 39237191
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. MLACP: machine-learning-based prediction of anticancer peptides.
    Manavalan B; Basith S; Shin TH; Choi S; Kim MO; Lee G
    Oncotarget; 2017 Sep; 8(44):77121-77136. PubMed ID: 29100375
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile.
    Jan A; Hayat M; Wedyan M; Alturki R; Gazzawe F; Ali H; Alarfaj FK
    Comput Biol Med; 2022 Dec; 151(Pt A):106311. PubMed ID: 36410097
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach.
    Kilimci ZH; Yalcin M
    Artif Intell Med; 2024 Oct; 156():102951. PubMed ID: 39173421
    [TBL] [Abstract][Full Text] [Related]  

  • 18. CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides.
    Burdukiewicz M; Sidorczuk K; Rafacz D; Pietluch F; Bąkała M; Słowik J; Gagat P
    Pharmaceutics; 2020 Oct; 12(11):. PubMed ID: 33142753
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.
    Zakharova E; Orsi M; Capecchi A; Reymond JL
    ChemMedChem; 2022 Sep; 17(17):e202200291. PubMed ID: 35880810
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 12.