bacteria:t3e:software
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| bacteria:t3e:software [2026/09/09 08:03] – [Further Reading] rkoebnik | bacteria:t3e:software [2026/09/09 08:18] (current) – [Further Reading] rkoebnik | ||
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| ===== Further Reading ===== | ===== Further Reading ===== | ||
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| - | Arroyo-Velez N, González-Fuente M, Peeters N, Lauber E, Noël LD (2020). From effectors to effectomes: Are functional studies of individual effectors enough to decipher plant pathogen infectious strategies? PLoS Pathog. 16: e1009059. DOI: [[https:// | ||
| Chan DTC, Agarwal V, Baltrus DA, Dillon MM (2025). Unified classification of the type III secreted effectors of bacterial plant pathogens to advance phytopathology research. Phytopathology 115: 1315-1328. DOI: [[https:// | Chan DTC, Agarwal V, Baltrus DA, Dillon MM (2025). Unified classification of the type III secreted effectors of bacterial plant pathogens to advance phytopathology research. Phytopathology 115: 1315-1328. DOI: [[https:// | ||
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| Noël LD, Denancé N, Szurek B (2013). Predicting promoters targeted by TAL effectors in plant genomes: from dream to reality. Front. Plant Sci. 4: 333. DOI: [[https:// | Noël LD, Denancé N, Szurek B (2013). Predicting promoters targeted by TAL effectors in plant genomes: from dream to reality. Front. Plant Sci. 4: 333. DOI: [[https:// | ||
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| + | Rosić I, Nikolić I (2026). Challenges and opportunities in type III secretion system effector prediction. Open Biol. 16: 250485. DOI: [[https:// | ||
| Wei L, He S, Fan Z (2026). Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges. Front. Chem. 14: 1810136. DOI: [[https:// | Wei L, He S, Fan Z (2026). Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges. Front. Chem. 14: 1810136. DOI: [[https:// | ||
bacteria/t3e/software.1788937431.txt.gz · Last modified: 2026/09/09 08:03 by rkoebnik
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