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 *

128 related articles for article (PubMed ID: 26120140)

  • 1. BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.
    Dong X; Lu X; Zhang Z
    Database (Oxford); 2015; 2015():bav064. PubMed ID: 26120140
    [TBL] [Abstract][Full Text] [Related]  

  • 2. T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors.
    Hui X; Chen Z; Lin M; Zhang J; Hu Y; Zeng Y; Cheng X; Ou-Yang L; Sun MA; White AP; Wang Y
    mSystems; 2020 Aug; 5(4):. PubMed ID: 32753503
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Terminal reassortment drives the quantum evolution of type III effectors in bacterial pathogens.
    Stavrinides J; Ma W; Guttman DS
    PLoS Pathog; 2006 Oct; 2(10):e104. PubMed ID: 17040127
    [TBL] [Abstract][Full Text] [Related]  

  • 4. T3SEdb: data warehousing of virulence effectors secreted by the bacterial Type III Secretion System.
    Tay DM; Govindarajan KR; Khan AM; Ong TY; Samad HM; Soh WW; Tong M; Zhang F; Tan TW
    BMC Bioinformatics; 2010 Oct; 11 Suppl 7(Suppl 7):S4. PubMed ID: 21106126
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identifying
    Lee AH; Bastedo DP; Youn JY; Lo T; Middleton MA; Kireeva I; Lee JY; Sharifpoor S; Baryshnikova A; Zhang J; Wang PW; Peisajovich SG; Constanzo M; Andrews BJ; Boone CM; Desveaux D; Guttman DS
    G3 (Bethesda); 2019 Feb; 9(2):535-547. PubMed ID: 30573466
    [TBL] [Abstract][Full Text] [Related]  

  • 6. DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.
    Xue L; Tang B; Chen W; Luo J
    Bioinformatics; 2019 Jun; 35(12):2051-2057. PubMed ID: 30407530
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.
    Jing R; Wen T; Liao C; Xue L; Liu F; Yu L; Luo J
    NAR Genom Bioinform; 2021 Dec; 3(4):lqab086. PubMed ID: 34617013
    [TBL] [Abstract][Full Text] [Related]  

  • 8. EP3: an ensemble predictor that accurately identifies type III secreted effectors.
    Li J; Wei L; Guo F; Zou Q
    Brief Bioinform; 2021 Mar; 22(2):1918-1928. PubMed ID: 32043137
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bastion3: a two-layer ensemble predictor of type III secreted effectors.
    Wang J; Li J; Yang B; Xie R; Marquez-Lago TT; Leier A; Hayashida M; Akutsu T; Zhang Y; Chou KC; Selkrig J; Zhou T; Song J; Lithgow T
    Bioinformatics; 2019 Jun; 35(12):2017-2028. PubMed ID: 30388198
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Molecular Evolution of
    Dillon MM; Almeida RND; Laflamme B; Martel A; Weir BS; Desveaux D; Guttman DS
    Front Plant Sci; 2019; 10():418. PubMed ID: 31024592
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Die another day: Molecular mechanisms of effector-triggered immunity elicited by type III secreted effector proteins.
    Schreiber KJ; Baudin M; Hassan JA; Lewis JD
    Semin Cell Dev Biol; 2016 Aug; 56():124-133. PubMed ID: 27166224
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ACNNT3: Attention-CNN Framework for Prediction of Sequence-Based Bacterial Type III Secreted Effectors.
    Li J; Li Z; Luo J; Yao Y
    Comput Math Methods Med; 2020; 2020():3974598. PubMed ID: 32328150
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Host-pathogen interplay and the evolution of bacterial effectors.
    Stavrinides J; McCann HC; Guttman DS
    Cell Microbiol; 2008 Feb; 10(2):285-92. PubMed ID: 18034865
    [TBL] [Abstract][Full Text] [Related]  

  • 14. DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.
    Yu L; Liu F; Li Y; Luo J; Jing R
    Front Microbiol; 2021; 12():605782. PubMed ID: 33552038
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models.
    Ran Z; Wang C; Sun H; Pan S; Li F
    IEEE J Biomed Health Inform; 2024 Sep; 28(9):5649-5657. PubMed ID: 38865232
    [TBL] [Abstract][Full Text] [Related]  

  • 16. What the Wild Things Do: Mechanisms of Plant Host Manipulation by Bacterial Type III-Secreted Effector Proteins.
    Schreiber KJ; Chau-Ly IJ; Lewis JD
    Microorganisms; 2021 May; 9(5):. PubMed ID: 34064647
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Catalytic domain of the diversified Pseudomonas syringae type III effector HopZ1 determines the allelic specificity in plant hosts.
    Morgan RL; Zhou H; Lehto E; Nguyen N; Bains A; Wang X; Ma W
    Mol Microbiol; 2010 Apr; 76(2):437-55. PubMed ID: 20233307
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Expression and Functional Analysis of the Type III Secretion System Effector Repertoire of the Xylem Pathogen
    Olawole OI; Gleason ML; Beattie GA
    Mol Plant Microbe Interact; 2022 Sep; 35(9):768-778. PubMed ID: 35471035
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A new feature selection method for computational prediction of type III secreted effectors.
    Yang Y; Qi S
    Int J Data Min Bioinform; 2014; 10(4):440-54. PubMed ID: 25946888
    [TBL] [Abstract][Full Text] [Related]  

  • 20. α-Helices in the Type III Secretion Effectors: A Prevalent Feature with Versatile Roles.
    Gazi AD; Kokkinidis M; Fadouloglou VE
    Int J Mol Sci; 2021 May; 22(11):. PubMed ID: 34063760
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.