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: 39362624)

  • 1. iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention.
    Wang X; Zhang Z; Liu C
    J Mol Biol; 2024 Nov; 436(22):168810. PubMed ID: 39362624
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.
    Charoenkwan P; Chiangjong W; Lee VS; Nantasenamat C; Hasan MM; Shoombuatong W
    Sci Rep; 2021 Feb; 11(1):3017. PubMed ID: 33542286
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. iACP: a sequence-based tool for identifying anticancer peptides.
    Chen W; Ding H; Feng P; Lin H; Chou KC
    Oncotarget; 2016 Mar; 7(13):16895-909. PubMed ID: 26942877
    [TBL] [Abstract][Full Text] [Related]  

  • 7. AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.
    Zhang S; Zhao Y; Liang Y
    Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38452348
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.
    Arif M; Musleh S; Fida H; Alam T
    Sci Rep; 2024 Jul; 14(1):16992. PubMed ID: 39043738
    [TBL] [Abstract][Full Text] [Related]  

  • 10. mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.
    Sangaraju VK; Pham NT; Wei L; Yu X; Manavalan B
    J Mol Biol; 2024 Sep; 436(17):168687. PubMed ID: 39237191
    [TBL] [Abstract][Full Text] [Related]  

  • 11. ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction.
    Wang X; Wang S
    Comput Biol Chem; 2024 Oct; 112():108141. PubMed ID: 38996756
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. iACP-MultiCNN: Multi-channel CNN based anticancer peptides identification.
    Aziz AZB; Hasan MAM; Ahmad S; Mamun MA; Shin J; Hossain MR
    Anal Biochem; 2022 Aug; 650():114707. PubMed ID: 35568159
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.
    Zhao Y; Wang S; Fei W; Feng Y; Shen L; Yang X; Wang M; Wu M
    Int J Mol Sci; 2021 May; 22(11):. PubMed ID: 34073203
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.
    Zhang M; Zhou J; Wang X; Wang X; Ge F
    BMC Bioinformatics; 2024 Nov; 25(1):352. PubMed ID: 39528950
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ACP-ML: A sequence-based method for anticancer peptide prediction.
    Bian J; Liu X; Dong G; Hou C; Huang S; Zhang D
    Comput Biol Med; 2024 Mar; 170():108063. PubMed ID: 38301519
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.
    Rao B; Zhou C; Zhang G; Su R; Wei L
    Brief Bioinform; 2020 Sep; 21(5):1846-1855. PubMed ID: 31729528
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach.
    Karim T; Shaon MSH; Sultan MF; Hasan MZ; Kafy AA
    Comput Biol Med; 2024 Feb; 169():107915. PubMed ID: 38171261
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion.
    Cao R; Wang M; Bin Y; Zheng C
    PeerJ; 2021; 9():e11906. PubMed ID: 34414035
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.