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Journal Abstract Search

169 related items for PubMed ID: 24267033

  • 1. Prediction of essential proteins based on gene expression programming.
    Zhong J, Wang J, Peng W, Zhang Z, Pan Y.
    BMC Genomics; 2013; 14 Suppl 4(Suppl 4):S7. PubMed ID: 24267033
    [Abstract] [Full Text] [Related]

  • 2. Prediction of essential proteins based on subcellular localization and gene expression correlation.
    Fan Y, Tang X, Hu X, Wu W, Ping Q.
    BMC Bioinformatics; 2017 Dec 01; 18(Suppl 13):470. PubMed ID: 29219067
    [Abstract] [Full Text] [Related]

  • 3. 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
    [Abstract] [Full Text] [Related]

  • 4. 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]

  • 5. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
    You ZH, Yin Z, Han K, Huang DS, Zhou X.
    BMC Bioinformatics; 2010 Jun 24; 11():343. PubMed ID: 20573270
    [Abstract] [Full Text] [Related]

  • 6. 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]

  • 7. A novel method to predict essential proteins based on tensor and HITS algorithm.
    Zhang Z, Luo Y, Hu S, Li X, Wang L, Zhao B.
    Hum Genomics; 2020 Apr 06; 14(1):14. PubMed ID: 32252824
    [Abstract] [Full Text] [Related]

  • 8. UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks.
    Peng W, Wang J, Cheng Y, Lu Y, Wu F, Pan Y.
    IEEE/ACM Trans Comput Biol Bioinform; 2015 Apr 06; 12(2):276-88. PubMed ID: 26357216
    [Abstract] [Full Text] [Related]

  • 9. Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
    Zhang X, Xiao W, Hu X.
    PLoS One; 2018 Apr 06; 13(4):e0195410. PubMed ID: 29634727
    [Abstract] [Full Text] [Related]

  • 10. Prediction of essential proteins based on overlapping essential modules.
    Zhao B, Wang J, Li M, Wu FX, Pan Y.
    IEEE Trans Nanobioscience; 2014 Dec 06; 13(4):415-24. PubMed ID: 25122840
    [Abstract] [Full Text] [Related]

  • 11. A new method for predicting essential proteins based on participation degree in protein complex and subgraph density.
    Lei X, Yang X.
    PLoS One; 2018 Dec 06; 13(6):e0198998. PubMed ID: 29894517
    [Abstract] [Full Text] [Related]

  • 12. Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.
    Luo J, Qi Y.
    PLoS One; 2015 Dec 06; 10(6):e0131418. PubMed ID: 26125187
    [Abstract] [Full Text] [Related]

  • 13. 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
    [Abstract] [Full Text] [Related]

  • 14. A new computational strategy for predicting essential genes.
    Cheng J, Wu W, Zhang Y, Li X, Jiang X, Wei G, Tao S.
    BMC Genomics; 2013 Dec 21; 14():910. PubMed ID: 24359534
    [Abstract] [Full Text] [Related]

  • 15. A new computational strategy for identifying essential proteins based on network topological properties and biological information.
    Qin C, Sun Y, Dong Y.
    PLoS One; 2017 Dec 21; 12(7):e0182031. PubMed ID: 28753682
    [Abstract] [Full Text] [Related]

  • 16. 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 Dec 21; 9(1):e86805. PubMed ID: 24466248
    [Abstract] [Full Text] [Related]

  • 17. Prediction of essential genes in prokaryote based on artificial neural network.
    Xu L, Guo Z, Liu X.
    Genes Genomics; 2020 Jan 21; 42(1):97-106. PubMed ID: 31736009
    [Abstract] [Full Text] [Related]

  • 18. A deep learning framework for identifying essential proteins based on multiple biological information.
    Yue Y, Ye C, Peng PY, Zhai HX, Ahmad I, Xia C, Wu YZ, Zhang YH.
    BMC Bioinformatics; 2022 Aug 04; 23(1):318. PubMed ID: 35927611
    [Abstract] [Full Text] [Related]

  • 19. 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 Aug 04; 12(3):e0174638. PubMed ID: 28358836
    [Abstract] [Full Text] [Related]

  • 20. HELP: A computational framework for labelling and predicting human common and context-specific essential genes.
    Granata I, Maddalena L, Manzo M, Guarracino MR, Giordano M.
    PLoS Comput Biol; 2024 Sep 04; 20(9):e1012076. PubMed ID: 39331694
    [Abstract] [Full Text] [Related]

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