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Journal Abstract Search

169 related items for PubMed ID: 31736009

  • 1. Prediction of essential genes in prokaryote based on artificial neural network.
    Xu L, Guo Z, Liu X.
    Genes Genomics; 2020 Jan; 42(1):97-106. PubMed ID: 31736009
    [Abstract] [Full Text] [Related]

  • 2. Machine learning approach to gene essentiality prediction: a review.
    Aromolaran O, Aromolaran D, Isewon I, Oyelade J.
    Brief Bioinform; 2021 Sep 02; 22(5):. PubMed ID: 33842944
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  • 3. Predicting essential genes of 37 prokaryotes by combining information-theoretic features.
    Liu X, Luo Y, He T, Ren M, Xu Y.
    J Microbiol Methods; 2021 Sep 02; 188():106297. PubMed ID: 34343487
    [Abstract] [Full Text] [Related]

  • 4. Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species.
    Liu X, Wang BJ, Xu L, Tang HL, Xu GQ.
    PLoS One; 2017 Sep 02; 12(3):e0174638. PubMed ID: 28358836
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  • 5. Network-based features enable prediction of essential genes across diverse organisms.
    Azhagesan K, Ravindran B, Raman K.
    PLoS One; 2018 Sep 02; 13(12):e0208722. PubMed ID: 30543651
    [Abstract] [Full Text] [Related]

  • 6. DeepHE: Accurately predicting human essential genes based on deep learning.
    Zhang X, Xiao W, Xiao W.
    PLoS Comput Biol; 2020 Sep 02; 16(9):e1008229. PubMed ID: 32936825
    [Abstract] [Full Text] [Related]

  • 7. Training set selection for the prediction of essential genes.
    Cheng J, Xu Z, Wu W, Zhao L, Li X, Liu Y, Tao S.
    PLoS One; 2014 Sep 02; 9(1):e86805. PubMed ID: 24466248
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  • 9. EPGAT: Gene Essentiality Prediction With Graph Attention Networks.
    Schapke J, Tavares A, Recamonde-Mendoza M.
    IEEE/ACM Trans Comput Biol Bioinform; 2022 Sep 02; 19(3):1615-1626. PubMed ID: 33497339
    [Abstract] [Full Text] [Related]

  • 10. Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.
    Dai W, Chang Q, Peng W, Zhong J, Li Y.
    Genes (Basel); 2020 Jan 31; 11(2):. PubMed ID: 32023848
    [Abstract] [Full Text] [Related]

  • 11. Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
    Song K, Tong T, Wu F.
    Integr Biol (Camb); 2014 Apr 31; 6(4):460-9. PubMed ID: 24603751
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  • 12. Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information.
    Acencio ML, Lemke N.
    BMC Bioinformatics; 2009 Sep 16; 10():290. PubMed ID: 19758426
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  • 14. An integrated machine-learning model to predict prokaryotic essential genes.
    Deng J.
    Methods Mol Biol; 2015 Sep 16; 1279():137-51. PubMed ID: 25636617
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  • 19. Sequence-based information-theoretic features for gene essentiality prediction.
    Nigatu D, Sobetzko P, Yousef M, Henkel W.
    BMC Bioinformatics; 2017 Nov 09; 18(1):473. PubMed ID: 29121868
    [Abstract] [Full Text] [Related]

  • 20. Analysis and identification of essential genes in humans using topological properties and biological information.
    Yang L, Wang J, Wang H, Lv Y, Zuo Y, Li X, Jiang W.
    Gene; 2014 Nov 10; 551(2):138-51. PubMed ID: 25168893
    [Abstract] [Full Text] [Related]

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