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 *

128 related articles for article (PubMed ID: 34081438)

  • 1. Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set.
    Pinacho-Castellanos SA; García-Jacas CR; Gilson MK; Brizuela CA
    J Chem Inf Model; 2021 Jun; 61(6):3141-3157. PubMed ID: 34081438
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types.
    Xiao X; Shao YT; Cheng X; Stamatovic B
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34086856
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.
    Ayala-Ruano S; Marrero-Ponce Y; Aguilera-Mendoza L; Pérez N; Agüero-Chapin G; Antunes A; Aguilar AC
    ACS Omega; 2022 Dec; 7(50):46012-46036. PubMed ID: 36570318
    [TBL] [Abstract][Full Text] [Related]  

  • 5. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.
    Wei L; Zhou C; Su R; Zou Q
    Bioinformatics; 2019 Nov; 35(21):4272-4280. PubMed ID: 30994882
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Collection of antimicrobial peptides database and its derivatives: Applications and beyond.
    Waghu FH; Idicula-Thomas S
    Protein Sci; 2020 Jan; 29(1):36-42. PubMed ID: 31441165
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.
    Schaduangrat N; Nantasenamat C; Prachayasittikul V; Shoombuatong W
    Int J Mol Sci; 2019 Nov; 20(22):. PubMed ID: 31731751
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. Do deep learning models make a difference in the identification of antimicrobial peptides?
    García-Jacas CR; Pinacho-Castellanos SA; García-González LA; Brizuela CA
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35380616
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Empirical comparison of web-based antimicrobial peptide prediction tools.
    Gabere MN; Noble WS
    Bioinformatics; 2017 Jul; 33(13):1921-1929. PubMed ID: 28203715
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.
    Xiao X; Wang P; Lin WZ; Jia JH; Chou KC
    Anal Biochem; 2013 May; 436(2):168-77. PubMed ID: 23395824
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition.
    Feng J; Sun M; Liu C; Zhang W; Xu C; Wang J; Wang G; Wan S
    bioRxiv; 2024 Apr; ():. PubMed ID: 38712184
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.