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bacteria:t3e:software [2025/04/24 09:41] – rkoebnikbacteria:t3e:software [2025/06/05 11:22] (current) – [References] rkoebnik
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 ^Name ^Purpose ^URL ^Reference | ^Name ^Purpose ^URL ^Reference |
 +|Effectidor II |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2025  |
 +|PLM-T3SE |T3E prediction |  |Gao //et al.//, 2025  |
 |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022a  | |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022a  |
 |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022b  | |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022b  |
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 Fu X, Yang Y (2019). WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. Quant. Biol. 7: 293-301. DOI: [[https://doi.org/10.1007/s40484-019-0184-7|10.1007/s40484-019-0184-7]] Fu X, Yang Y (2019). WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. Quant. Biol. 7: 293-301. DOI: [[https://doi.org/10.1007/s40484-019-0184-7|10.1007/s40484-019-0184-7]]
 +
 +Gao M, Song C, Liu T (2025). PLM-T3SE: Accurate prediction of type III secretion effectors using protein language model embeddings. J. Cell. Biochem. 126: e30642. DOI: [[https://doi.org/10.1002/jcb.30642|10.1002/jcb.30642]]
  
 Goldberg T, Rost B, Bromberg Y (2016). Computational prediction shines light on type III secretion origins. Sci. Rep. 6: 34516. DOI: [[https://doi.org/10.1038/srep34516|10.1038/srep34516]] Goldberg T, Rost B, Bromberg Y (2016). Computational prediction shines light on type III secretion origins. Sci. Rep. 6: 34516. DOI: [[https://doi.org/10.1038/srep34516|10.1038/srep34516]]
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 Wagner N, Avram O, Gold-Binshtok D, Zerah B, Teper D, Pupko T (2022b). Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors. Bioinformatics 38: 2341-2343. DOI: [[https://doi.org/10.1093/bioinformatics/btac087|10.1093/bioinformatics/btac087]] Wagner N, Avram O, Gold-Binshtok D, Zerah B, Teper D, Pupko T (2022b). Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors. Bioinformatics 38: 2341-2343. DOI: [[https://doi.org/10.1093/bioinformatics/btac087|10.1093/bioinformatics/btac087]]
 +
 +Wagner N, Baumer E, Lyubman I, Shimony Y, Bracha N, Martins L, Potnis N, Chang JH, Teper D, Koebnik R, Pupko T (2025). Effectidor II: A pan-genomic AI-based algorithm for the prediction of type III secretion system effectors. Bioinformatics 41: btaf272. DOI: [[https://doi.org/10.1093/bioinformatics/btaf272|10.1093/bioinformatics/btaf272]]
  
 Wagner S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: [[https://doi.org/10.1007/82_2020_210|10.1007/82_2020_210]] Wagner S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: [[https://doi.org/10.1007/82_2020_210|10.1007/82_2020_210]]
bacteria/t3e/software.1745484103.txt.gz · Last modified: 2025/04/24 09:41 by rkoebnik

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