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 *

157 related articles for article (PubMed ID: 39342017)

  • 1. HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.
    Zhao Y; Zhang S; Liang Y
    Sci Rep; 2024 Sep; 14(1):22518. PubMed ID: 39342017
    [TBL] [Abstract][Full Text] [Related]  

  • 2. HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information.
    Yang S; Xu P
    Anal Biochem; 2024 Jul; 690():115523. PubMed ID: 38552762
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential.
    Almotairi S; Badr E; Abdelbaky I; Elhakeem M; Abdul Salam M
    Sci Rep; 2024 Jun; 14(1):14263. PubMed ID: 38902287
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.
    Lin S; Wang Y; Zhang L; Chu Y; Liu Y; Fang Y; Jiang M; Wang Q; Zhao B; Xiong Y; Wei DQ
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34671814
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.
    Li J; Sun L; Liu L; Li Z
    Front Genet; 2024; 15():1381997. PubMed ID: 38770418
    [TBL] [Abstract][Full Text] [Related]  

  • 9. AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.
    Salem M; Keshavarzi Arshadi A; Yuan JS
    BMC Bioinformatics; 2022 Sep; 23(1):389. PubMed ID: 36163001
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.
    Zhao W; Yu Y; Liu G; Liang Y; Xu D; Feng X; Guan R
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38762789
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development of a defibrinated human blood hemolysis assay for rapid testing of hemolytic activity compared to computational prediction.
    Carpenter AM; van Hoek ML
    J Immunol Methods; 2024 Jun; 529():113670. PubMed ID: 38604530
    [TBL] [Abstract][Full Text] [Related]  

  • 12. NeuroPpred-SHE: An interpretable neuropeptides prediction model based on selected features from hand-crafted features and embeddings of T5 model.
    Wen J; Ding Z; Wei Z; Xia H; Zhang Y; Zhu X
    Comput Biol Med; 2024 Oct; 181():109048. PubMed ID: 39182368
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.
    Li GY; Chen J; Jang SI; Gong K; Li Q
    Med Phys; 2024 Mar; 51(3):2096-2107. PubMed ID: 37776263
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.
    Fang Y; Luo M; Ren Z; Wei L; Wei DQ
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 39038935
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Biomedical named entity recognition based on multi-cross attention feature fusion.
    Zheng D; Han R; Yu F; Li Y
    PLoS One; 2024; 19(5):e0304329. PubMed ID: 38805478
    [TBL] [Abstract][Full Text] [Related]  

  • 16. EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.
    Sharma R; Shrivastava S; Singh SK; Kumar A; Singh AK; Saxena S
    IEEE J Biomed Health Inform; 2023 Apr; PP():. PubMed ID: 37018101
    [TBL] [Abstract][Full Text] [Related]  

  • 17. iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.
    Akbar S; Zou Q; Raza A; Alarfaj FK
    Artif Intell Med; 2024 May; 151():102860. PubMed ID: 38552379
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides.
    He W; Wang Y; Cui L; Su R; Wei L
    Bioinformatics; 2021 Dec; 37(24):4684-4693. PubMed ID: 34323948
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EMAT: Efficient feature fusion network for visual tracking via optimized multi-head attention.
    Wang J; Lai C; Wang Y; Zhang W
    Neural Netw; 2024 Apr; 172():106110. PubMed ID: 38237443
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Integrated convolution and self-attention for improving peptide toxicity prediction.
    Jiao S; Ye X; Sakurai T; Zou Q; Liu R
    Bioinformatics; 2024 May; 40(5):. PubMed ID: 38696758
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.