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 *

119 related articles for article (PubMed ID: 38972032)

  • 1. Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.
    Zhong G; Liu H; Deng L
    Interdiscip Sci; 2024 Jul; ():. PubMed ID: 38972032
    [TBL] [Abstract][Full Text] [Related]  

  • 2. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features.
    Zhuang J; Gao W; Su R
    J Bioinform Comput Biol; 2024 Feb; 22(1):2450001. PubMed ID: 38406833
    [TBL] [Abstract][Full Text] [Related]  

  • 3. AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.
    Lee H; Lee S; Lee I; Nam H
    Protein Sci; 2023 Jan; 32(1):e4529. PubMed ID: 36461699
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.
    Yan K; Lv H; Guo Y; Peng W; Liu B
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36342186
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Antimicrobial peptide identification using multi-scale convolutional network.
    Su X; Xu J; Yin Y; Quan X; Zhang H
    BMC Bioinformatics; 2019 Dec; 20(1):730. PubMed ID: 31870282
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.
    Wei L; Ye X; Sakurai T; Mu Z; Wei L
    Bioinformatics; 2022 Mar; 38(6):1514-1524. PubMed ID: 34999757
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.
    Yuan Q; Chen K; Yu Y; Le NQK; Chua MCH
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36642410
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.
    Zhang Y; Yu S; Xie R; Li J; Leier A; Marquez-Lago TT; Akutsu T; Smith AI; Ge Z; Wang J; Lithgow T; Song J
    Bioinformatics; 2020 Feb; 36(3):704-712. PubMed ID: 31393553
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.
    Vos G; Trinh K; Sarnyai Z; Rahimi Azghadi M
    J Biomed Inform; 2023 Dec; 148():104556. PubMed ID: 38048895
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep learning improves antimicrobial peptide recognition.
    Veltri D; Kamath U; Shehu A
    Bioinformatics; 2018 Aug; 34(16):2740-2747. PubMed ID: 29590297
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.
    Sharma R; Shrivastava S; Kumar Singh S; Kumar A; Saxena S; Kumar Singh R
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34259329
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fuse feeds as one: cross-modal framework for general identification of AMPs.
    Zhang W; Xu Y; Wang A; Chen G; Zhao J
    Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37779248
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 6.