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 *

292 related articles for article (PubMed ID: 33494403)

  • 1. Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs.
    Lertampaiporn S; Vorapreeda T; Hongsthong A; Thammarongtham C
    Genes (Basel); 2021 Jan; 12(2):. PubMed ID: 33494403
    [TBL] [Abstract][Full Text] [Related]  

  • 2. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning.
    Lv H; Yan K; Guo Y; Zou Q; Hesham AE; Liu B
    Comput Biol Med; 2022 Jul; 146():105577. PubMed ID: 35576825
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A review on antimicrobial peptides databases and the computational tools.
    Ramazi S; Mohammadi N; Allahverdi A; Khalili E; Abdolmaleki P
    Database (Oxford); 2022 Mar; 2022():. PubMed ID: 35305010
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.
    Singh O; Hsu WL; Su EC
    BMC Bioinformatics; 2021 Jul; 22(1):389. PubMed ID: 34330209
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Antimicrobial peptides recognition using weighted physicochemical property encoding.
    Na S; Wannigama DL; Saethang T
    J Bioinform Comput Biol; 2023 Apr; 21(2):2350006. PubMed ID: 37120707
    [TBL] [Abstract][Full Text] [Related]  

  • 6. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.
    Xu J; Li F; Li C; Guo X; Landersdorfer C; Shen HH; Peleg AY; Li J; Imoto S; Yao J; Akutsu T; Song J
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37369638
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram.
    Burdukiewicz M; Sidorczuk K; Rafacz D; Pietluch F; Chilimoniuk J; Rödiger S; Gagat P
    Int J Mol Sci; 2020 Jun; 21(12):. PubMed ID: 32560350
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
    Meher PK; Sahu TK; Saini V; Rao AR
    Sci Rep; 2017 Feb; 7():42362. PubMed ID: 28205576
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computational resources and tools for antimicrobial peptides.
    Liu S; Fan L; Sun J; Lao X; Zheng H
    J Pept Sci; 2017 Jan; 23(1):4-12. PubMed ID: 27966278
    [TBL] [Abstract][Full Text] [Related]  

  • 10. AMAP: Hierarchical multi-label prediction of biologically active and antimicrobial peptides.
    Gull S; Shamim N; Minhas F
    Comput Biol Med; 2019 Apr; 107():172-181. PubMed ID: 30831306
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CAMP: a useful resource for research on antimicrobial peptides.
    Thomas S; Karnik S; Barai RS; Jayaraman VK; Idicula-Thomas S
    Nucleic Acids Res; 2010 Jan; 38(Database issue):D774-80. PubMed ID: 19923233
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides.
    Wani MA; Garg P; Roy KK
    Med Biol Eng Comput; 2021 Nov; 59(11-12):2397-2408. PubMed ID: 34632545
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.
    Xu J; Li F; Leier A; Xiang D; Shen HH; Marquez Lago TT; Li J; Yu DJ; Song J
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33774670
    [TBL] [Abstract][Full Text] [Related]  

  • 14. An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.
    Lin Y; Cai Y; Liu J; Lin C; Liu X
    BMC Bioinformatics; 2019 Jun; 20(Suppl 8):291. PubMed ID: 31182007
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.
    Vishnepolsky B; Gabrielian A; Rosenthal A; Hurt DE; Tartakovsky M; Managadze G; Grigolava M; Makhatadze GI; Pirtskhalava M
    J Chem Inf Model; 2018 May; 58(5):1141-1151. PubMed ID: 29716188
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides.
    Caprani MC; Healy J; Slattery O; O'Keeffe J
    Interdiscip Sci; 2021 Jun; 13(2):321-333. PubMed ID: 33978916
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Ensemble-AHTPpred: A Robust Ensemble Machine Learning Model Integrated With a New Composite Feature for Identifying Antihypertensive Peptides.
    Lertampaiporn S; Hongsthong A; Wattanapornprom W; Thammarongtham C
    Front Genet; 2022; 13():883766. PubMed ID: 35571042
    [TBL] [Abstract][Full Text] [Related]  

  • 18. CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
    Porto WF; Pires ÁS; Franco OL
    PLoS One; 2012; 7(12):e51444. PubMed ID: 23240023
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.
    Vishnepolsky B; Grigolava M; Managadze G; Gabrielian A; Rosenthal A; Hurt DE; Tartakovsky M; Pirtskhalava M
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35724561
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms.
    Chung CR; Jhong JH; Wang Z; Chen S; Wan Y; Horng JT; Lee TY
    Int J Mol Sci; 2020 Feb; 21(3):. PubMed ID: 32024233
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.