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 *

142 related articles for article (PubMed ID: 31957609)

  • 1. Progress in the development of antimicrobial peptide prediction tools.
    Ao C; Zhang Y; Li D; Zhao Y; Zou Q
    Curr Protein Pept Sci; 2020 Jan; ():. PubMed ID: 31957609
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides.
    Bournez C; Riool M; de Boer L; Cordfunke RA; de Best L; van Leeuwen R; Drijfhout JW; Zaat SAJ; van Westen GJP
    Antibiotics (Basel); 2023 Apr; 12(4):. PubMed ID: 37107088
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6.
    Bobde SS; Alsaab FM; Wang G; Van Hoek ML
    Front Microbiol; 2021; 12():715246. PubMed ID: 34867843
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria.
    Shao J; Zhao Y; Wei W; Vaisman II
    Front Microbiol; 2024; 15():1304044. PubMed ID: 38516021
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods.
    Teimouri H; Medvedeva A; Kolomeisky AB
    J Chem Inf Model; 2023 Mar; 63(6):1723-1733. PubMed ID: 36912047
    [TBL] [Abstract][Full Text] [Related]  

  • 11. sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.
    Pandey P; Srivastava A
    Proteins; 2025 Jan; 93(1):372-383. PubMed ID: 38520179
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Characterization and identification of antimicrobial peptides with different functional activities.
    Chung CR; Kuo TR; Wu LC; Lee TY; Horng JT
    Brief Bioinform; 2019 Jun; ():. PubMed ID: 31155657
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against
    Yan J; Zhang B; Zhou M; Campbell-Valois FX; Siu SWI
    mSystems; 2023 Aug; 8(4):e0034523. PubMed ID: 37431995
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. In vitro and in silico comparative evaluation of anti-Acinetobacter baumannii peptides.
    Sharma A; Rishi P; Gautam A; Gautam V; Tewari R
    J Microbiol Biotechnol; 2015 Oct; ():. PubMed ID: 26428729
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Machine Learning Prediction of Antimicrobial Peptides.
    Wang G; Vaisman II; van Hoek ML
    Methods Mol Biol; 2022; 2405():1-37. PubMed ID: 35298806
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Discovering new in silico tools for antimicrobial peptide prediction.
    Torrent M; Nogués MV; Boix E
    Curr Drug Targets; 2012 Aug; 13(9):1148-57. PubMed ID: 22664076
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.
    Gautam A; Sharma A; Jaiswal S; Fatma S; Arora V; Iquebal MA; Nandi S; Sundaray JK; Jayasankar P; Rai A; Kumar D
    Probiotics Antimicrob Proteins; 2016 Sep; 8(3):141-9. PubMed ID: 27141850
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 8.