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Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

J. Wang, B. Yang, Y. An, T. Marquez-Lago, A. Leier, J. Wilksch, Q. Hong, Y. Zhang, M. Hayashida, T. Akutsu, G. Webb, R. Strugnell, J. Song, and T. Lithgow. Briefings Bioinform., 20 (3): 931-951 (2019)

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Jiangning Gao

Song Song

Song-Hak Song-Schreff

Peng Song

Other publications of authors with the same name

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.J. Song, K. Burrage, Z. Yuan, and T. Huber. BMC Bioinform., (2006)Exploring drug combinations in genetic interaction network.Y. Wang, K. Xu, J. Song, and X. Zhao. BMC Bioinform., 13 (S-7): S7 (2012)DiCleave: a deep learning model for predicting human Dicer cleavage sites.L. Mu, J. Song, T. Akutsu, and T. Mori. BMC Bioinform., 25 (1): 13 (December 2024)Prediction of Multiple Types of RNA Modifications via Biological Language Model.Y. Zhang, F. Ge, F. Li, X. Yang, J. Song, and D. Yu. IEEE ACM Trans. Comput. Biol. Bioinform., 20 (5): 3205-3214 (September 2023)T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model.Y. Zhang, Y. Zhang, Y. Xiong, H. Wang, Z. Deng, J. Song, and H. Ou. Briefings Bioinform., (2022)Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.Y. Zhu, J. Hu, F. Ge, F. Li, J. Song, Y. Zhang, and D. Yu. Briefings Bioinform., (2021)ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.X. Wang, F. Li, J. Xu, J. Rong, G. Webb, Z. Ge, J. Li, and J. Song. Briefings Bioinform., (2022)BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria.J. Wang, J. Li, Y. Hou, W. Dai, R. Xie, T. Marquez-Lago, A. Leier, T. Zhou, V. Torres, I. Hay and 4 other author(s). Nucleic Acids Res., 49 (Database-Issue): D651-D659 (2021)Prediction of protein-RNA residue-base contacts using two-dimensional conditional random field with the lasso.M. Hayashida, M. Kamada, J. Song, and T. Akutsu. BMC Syst. Biol., 7 (S-2): S15 (2013)Predicting protein-RNA residue-base contacts using two-dimensional conditional random field.M. Hayashida, M. Kamada, J. Song, and T. Akutsu. ISB, page 152-157. IEEE, (2012)

BibSonomy

Disambiguation of "Song, Jiangning"

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Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

Please choose a person to relate this publication to

Jiangning Gao

Song Song

Song Song

Song-Hak Song-Schreff

Peng Song

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Song, Jiangning"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

Please choose a person to relate this publication to

Jiangning Gao

Song Song

Song Song

Song-Hak Song-Schreff

Peng Song

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.