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 *

249 related articles for article (PubMed ID: 33024236)

  • 1. Machine learning-guided discovery and design of non-hemolytic peptides.
    Plisson F; Ramírez-Sánchez O; Martínez-Hernández C
    Sci Rep; 2020 Oct; 10(1):16581. PubMed ID: 33024236
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.
    Boone K; Wisdom C; Camarda K; Spencer P; Tamerler C
    BMC Bioinformatics; 2021 May; 22(1):239. PubMed ID: 33975547
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Design, Engineering and Discovery of Novel α-Helical and β-Boomerang Antimicrobial Peptides against Drug Resistant Bacteria.
    Bhattacharjya S; Straus SK
    Int J Mol Sci; 2020 Aug; 21(16):. PubMed ID: 32796755
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Deep Learning for Novel Antimicrobial Peptide Design.
    Wang C; Garlick S; Zloh M
    Biomolecules; 2021 Mar; 11(3):. PubMed ID: 33810011
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Potent Activity of Hybrid Arthropod Antimicrobial Peptides Linked by Glycine Spacers.
    Tonk M; Valdés JJ; Cabezas-Cruz A; Vilcinskas A
    Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445625
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology.
    Sowers A; Wang G; Xing M; Li B
    Microorganisms; 2023 Apr; 11(5):. PubMed ID: 37317103
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Correlation between hemolytic activity, cytotoxicity and systemic in vivo toxicity of synthetic antimicrobial peptides.
    Greco I; Molchanova N; Holmedal E; Jenssen H; Hummel BD; Watts JL; Håkansson J; Hansen PR; Svenson J
    Sci Rep; 2020 Aug; 10(1):13206. PubMed ID: 32764602
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Antimicrobial peptide selection from Lippia spp leaf transcriptomes.
    Tavares LS; de Souza VC; Schmitz Nunes V; Nascimento Silva O; de Souza GT; Farinazzo Marques L; Capriles Goliatt PVZ; Facio Viccini L; Franco OL; de Oliveira Santos M
    Peptides; 2020 Jul; 129():170317. PubMed ID: 32333997
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Design of new truncated derivatives based on direct and reverse mirror repeats of first six residues of Caerin 4 antimicrobial peptide and evaluation of their activity and cytotoxicity.
    Madanchi H; Sardari S; Shajiee H; Taherian S; Ashkar M; Johari B; Shabani AA; Sharafi S
    Chem Biol Drug Des; 2020 Aug; 96(2):801-811. PubMed ID: 32259385
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detecting antimicrobial peptides by exploring the mutual information of their sequences.
    Tripathi V; Tripathi P
    J Biomol Struct Dyn; 2020 Oct; 38(17):5037-5043. PubMed ID: 31760879
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.
    Yaseen A; Gull S; Akhtar N; Amin I; Minhas F
    J Bioinform Comput Biol; 2021 Oct; 19(5):2150021. PubMed ID: 34353244
    [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. Machine learning designs non-hemolytic antimicrobial peptides.
    Capecchi A; Cai X; Personne H; Köhler T; van Delden C; Reymond JL
    Chem Sci; 2021 Jul; 12(26):9221-9232. PubMed ID: 34349895
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Engineering antimicrobial peptides with improved antimicrobial and hemolytic activities.
    Zhao J; Zhao C; Liang G; Zhang M; Zheng J
    J Chem Inf Model; 2013 Dec; 53(12):3280-96. PubMed ID: 24279498
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.
    Khabbaz H; Karimi-Jafari MH; Saboury AA; BabaAli B
    BMC Bioinformatics; 2021 Nov; 22(1):549. PubMed ID: 34758751
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Mapping membrane activity in undiscovered peptide sequence space using machine learning.
    Lee EY; Fulan BM; Wong GC; Ferguson AL
    Proc Natl Acad Sci U S A; 2016 Nov; 113(48):13588-13593. PubMed ID: 27849600
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.