270 related articles for article (PubMed ID: 30407530)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. Computational prediction of type III secreted proteins from gram-negative bacteria.
Yang Y; Zhao J; Morgan RL; Ma W; Jiang T
BMC Bioinformatics; 2010 Jan; 11 Suppl 1(Suppl 1):S47. PubMed ID: 20122221
[TBL] [Abstract][Full Text] [Related]
8. Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles.
Yang X; Guo Y; Luo J; Pu X; Li M
PLoS One; 2013; 8(12):e84439. PubMed ID: 24391954
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes.
Hobbs CK; Porter VL; Stow ML; Siame BA; Tsang HH; Leung KY
BMC Genomics; 2016 Dec; 17(1):1048. PubMed ID: 27993130
[TBL] [Abstract][Full Text] [Related]
13. Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini.
Wang Y; Guo Y; Pu X; Li M
J Comput Aided Mol Des; 2017 Nov; 31(11):1029-1038. PubMed ID: 29127583
[TBL] [Abstract][Full Text] [Related]
14. iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.
Ding C; Han H; Li Q; Yang X; Liu T
Comput Math Methods Med; 2021; 2021():6690299. PubMed ID: 33505516
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.
Wang J; Yang B; Leier A; Marquez-Lago TT; Hayashida M; Rocker A; Zhang Y; Akutsu T; Chou KC; Strugnell RA; Song J; Lithgow T
Bioinformatics; 2018 Aug; 34(15):2546-2555. PubMed ID: 29547915
[TBL] [Abstract][Full Text] [Related]
19. DeepSF: deep convolutional neural network for mapping protein sequences to folds.
Hou J; Adhikari B; Cheng J
Bioinformatics; 2018 Apr; 34(8):1295-1303. PubMed ID: 29228193
[TBL] [Abstract][Full Text] [Related]
20. High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles.
Wang Y; Zhang Q; Sun MA; Guo D
Bioinformatics; 2011 Mar; 27(6):777-84. PubMed ID: 21233168
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
