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 *

130 related articles for article (PubMed ID: 37126975)

  • 1. DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes.
    Attique M; Alkhalifah T; Alturise F; Khan YD
    Comput Biol Chem; 2023 Jun; 104():107874. PubMed ID: 37126975
    [TBL] [Abstract][Full Text] [Related]  

  • 2. LBCEPred: a machine learning model to predict linear B-cell epitopes.
    Alghamdi W; Attique M; Alzahrani E; Ullah MZ; Khan YD
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35262658
    [TBL] [Abstract][Full Text] [Related]  

  • 3. DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes.
    Qi Y; Zheng P; Huang G
    Front Microbiol; 2023; 14():1117027. PubMed ID: 36910218
    [TBL] [Abstract][Full Text] [Related]  

  • 4. iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction.
    Manavalan B; Govindaraj RG; Shin TH; Kim MO; Lee G
    Front Immunol; 2018; 9():1695. PubMed ID: 30100904
    [TBL] [Abstract][Full Text] [Related]  

  • 5. NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.
    Xu H; Zhao Z
    Genomics Proteomics Bioinformatics; 2022 Oct; 20(5):1002-1012. PubMed ID: 36526218
    [TBL] [Abstract][Full Text] [Related]  

  • 6. EpiDope: a deep neural network for linear B-cell epitope prediction.
    Collatz M; Mock F; Barth E; Hölzer M; Sachse K; Marz M
    Bioinformatics; 2021 May; 37(4):448-455. PubMed ID: 32915967
    [TBL] [Abstract][Full Text] [Related]  

  • 7. BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.
    La Marca AF; Lopes RDS; Lotufo ADP; Bartholomeu DC; Minussi CR
    Sensors (Basel); 2022 May; 22(11):. PubMed ID: 35684648
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Pretoria: An effective computational approach for accurate and high-throughput identification of CD8
    Charoenkwan P; Schaduangrat N; Pham NT; Manavalan B; Shoombuatong W
    Int J Biol Macromol; 2023 May; 238():124228. PubMed ID: 36996953
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature.
    Zhang W; Xiong Y; Zhao M; Zou H; Ye X; Liu J
    BMC Bioinformatics; 2011 Aug; 12():341. PubMed ID: 21846404
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A machine-learning approach for predicting B-cell epitopes.
    Rubinstein ND; Mayrose I; Pupko T
    Mol Immunol; 2009 Feb; 46(5):840-7. PubMed ID: 18947876
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.
    Lu S; Li Y; Ma Q; Nan X; Zhang S
    Front Immunol; 2022; 13():890943. PubMed ID: 35844532
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. DRREP: deep ridge regressed epitope predictor.
    Sher G; Zhi D; Zhang S
    BMC Genomics; 2017 Oct; 18(Suppl 6):676. PubMed ID: 28984193
    [TBL] [Abstract][Full Text] [Related]  

  • 14. LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings.
    Liu Y; Liu Y; Wang S; Zhu X
    Interdiscip Sci; 2023 Jun; 15(2):293-305. PubMed ID: 36646842
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The applications of deep learning algorithms on in silico druggable proteins identification.
    Yu L; Xue L; Liu F; Li Y; Jing R; Luo J
    J Adv Res; 2022 Nov; 41():219-231. PubMed ID: 36328750
    [TBL] [Abstract][Full Text] [Related]  

  • 16. AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.
    Huang Y; Zhang Z; Zhou Y
    Front Immunol; 2022; 13():1053617. PubMed ID: 36618397
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of B-cell epitopes using evolutionary information and propensity scales.
    Lin SY; Cheng CW; Su EC
    BMC Bioinformatics; 2013; 14 Suppl 2(Suppl 2):S10. PubMed ID: 23484214
    [TBL] [Abstract][Full Text] [Related]  

  • 18. SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence.
    Dalkas GA; Rooman M
    BMC Bioinformatics; 2017 Feb; 18(1):95. PubMed ID: 28183272
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.
    Mahmud SMH; Goh KOM; Hosen MF; Nandi D; Shoombuatong W
    Sci Rep; 2024 Feb; 14(1):2961. PubMed ID: 38316843
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features.
    Huang JH; Wen M; Tang LJ; Xie HL; Fu L; Liang YZ; Lu HM
    Biochimie; 2014 Aug; 103():1-6. PubMed ID: 24721579
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.