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 *

146 related articles for article (PubMed ID: 35667299)

  • 1. Integrating multiple sequence features for identifying anticancer peptides.
    Zou H; Yang F; Yin Z
    Comput Biol Chem; 2022 Aug; 99():107711. PubMed ID: 35667299
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion.
    Zou H; Yang F; Yin Z
    Immunogenetics; 2022 Oct; 74(5):447-454. PubMed ID: 35246701
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of tumor homing peptides by utilizing hybrid feature representation.
    Zou H; Yang F; Yin Z
    J Biomol Struct Dyn; 2023 May; 41(8):3405-3412. PubMed ID: 35262448
    [TBL] [Abstract][Full Text] [Related]  

  • 4. iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm.
    Yu Z; Yin Z; Zou H
    J Bioinform Comput Biol; 2023 Oct; 21(5):2350023. PubMed ID: 37899353
    [TBL] [Abstract][Full Text] [Related]  

  • 5. iDPPIV-SI: identifying dipeptidyl peptidase IV inhibitory peptides by using multiple sequence information.
    Zou H
    J Biomol Struct Dyn; 2024; 42(4):2144-2152. PubMed ID: 37125813
    [TBL] [Abstract][Full Text] [Related]  

  • 6. m7G-DPP: Identifying N7-methylguanosine sites based on dinucleotide physicochemical properties of RNA.
    Zou H; Yin Z
    Biophys Chem; 2021 Dec; 279():106697. PubMed ID: 34628276
    [TBL] [Abstract][Full Text] [Related]  

  • 7. iDHS-DT: Identifying DNase I hypersensitive sites by integrating DNA dinucleotide and trinucleotide information.
    Zou H; Yang F; Yin Z
    Biophys Chem; 2022 Feb; 281():106717. PubMed ID: 34798459
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Effective identification and differential analysis of anticancer peptides.
    Zhang L; Hu X; Xiao K; Kong L
    Biosystems; 2024 Jul; 241():105246. PubMed ID: 38848816
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identifying N7-methylguanosine sites by integrating multiple features.
    Zou H; Yang F; Yin Z
    Biopolymers; 2022 Feb; 113(2):e23480. PubMed ID: 34709657
    [TBL] [Abstract][Full Text] [Related]  

  • 10. iRNA5hmC-HOC: High-order correlation information for identifying RNA 5-hydroxymethylcytosine modification.
    Zou H
    J Bioinform Comput Biol; 2022 Aug; 20(4):2250017. PubMed ID: 35918795
    [TBL] [Abstract][Full Text] [Related]  

  • 11. ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.
    Ahmed S; Muhammod R; Khan ZH; Adilina S; Sharma A; Shatabda S; Dehzangi A
    Sci Rep; 2021 Dec; 11(1):23676. PubMed ID: 34880291
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Integrating temporal and spatial variabilities for identifying ion binding proteins in phage.
    Zou H; Yu Z; Yin Z
    J Bioinform Comput Biol; 2023 Jun; 21(3):2350010. PubMed ID: 37325864
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Integrating Low-Order and High-Order Correlation Information for Identifying Phage Virion Proteins.
    Zou H; Yu W
    J Comput Biol; 2023 Oct; 30(10):1131-1143. PubMed ID: 37729064
    [TBL] [Abstract][Full Text] [Related]  

  • 14. iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree.
    Liang Y; Ma X
    SAR QSAR Environ Res; 2023 Jan; 34(1):1-19. PubMed ID: 36562289
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides.
    Wan Y; Wang Z; Lee TY
    BMC Bioinformatics; 2021 May; 22(1):286. PubMed ID: 34051755
    [TBL] [Abstract][Full Text] [Related]  

  • 16. DRACP: a novel method for identification of anticancer peptides.
    Zhao T; Hu Y; Zang T
    BMC Bioinformatics; 2020 Dec; 21(Suppl 16):559. PubMed ID: 33323099
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides.
    Feng G; Yao H; Li C; Liu R; Huang R; Fan X; Ge R; Miao Q
    Comput Biol Med; 2022 Jun; 145():105459. PubMed ID: 35358753
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Anticancer peptides prediction with deep representation learning features.
    Lv Z; Cui F; Zou Q; Zhang L; Xu L
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33529337
    [TBL] [Abstract][Full Text] [Related]  

  • 19. ACP_MS: prediction of anticancer peptides based on feature extraction.
    Zhou C; Peng D; Liao B; Jia R; Wu F
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36326080
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.
    Bhattarai S; Kim KS; Tayara H; Chong KT
    Int J Mol Sci; 2022 Oct; 23(20):. PubMed ID: 36293050
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.