Jianzhu Ma
Cited by
Cited by
Protein secondary structure prediction using deep convolutional neural fields
S Wang, J Peng, J Ma, J Xu
Scientific reports 6 (1), 1-11, 2016
RaptorX server: a resource for template-based protein structure modeling
M Källberg, G Margaryan, S Wang, J Ma, J Xu
Protein structure prediction, 17-27, 2014
Using deep learning to model the hierarchical structure and function of a cell
J Ma, MK Yu, S Fong, K Ono, E Sage, B Demchak, R Sharan, T Ideker
Nature methods 15 (4), 290, 2018
Protein structure alignment beyond spatial proximity
S Wang, J Ma, J Peng, J Xu
Scientific reports 3 (1), 1-7, 2013
Protein threading using context-specific alignment potential
J Ma, S Wang, F Zhao, J Xu
Bioinformatics 29 (13), i257-i265, 2013
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning
J Ma, S Wang, Z Wang, J Xu
Bioinformatics 31 (21), 3506-3513, 2015
A conditional neural fields model for protein threading
J Ma, J Peng, S Wang, J Xu
Bioinformatics 28 (12), i59-i66, 2012
Visible machine learning for biomedicine
KY Michael, J Ma, J Fisher, JF Kreisberg, BJ Raphael, T Ideker
Cell 173 (7), 1562-1565, 2018
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields
S Wang, J Ma, J Xu
Bioinformatics 32 (17), i672-i679, 2016
MRFalign: protein homology detection through alignment of Markov random fields
J Ma, S Wang, Z Wang, J Xu
PLoS Comput Biol 10 (3), e1003500, 2014
DeepCNF-D: predicting protein order/disorder regions by weighted deep convolutional neural fields
S Wang, S Weng, J Ma, Q Tang
International journal of molecular sciences 16 (8), 17315-17330, 2015
ModuleAlign: module-based global alignment of protein–protein interaction networks
S Hashemifar, J Ma, H Naveed, S Canzar, J Xu
Bioinformatics 32 (17), i658-i664, 2016
Algorithms, applications, and challenges of protein structure alignment
J Ma, S Wang
Advances in Protein Chemistry and Structural Biology 94, 121-175, 2014
Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation
J Zhou, J Ma, Y Chen, C Cheng, B Bao, J Peng, TJ Sejnowski, JR Dixon, ...
Proceedings of the National Academy of Sciences 116 (28), 14011-14018, 2019
AcconPred: Predicting solvent accessibility and contact number simultaneously by a multitask learning framework under the conditional neural fields model
J Ma, S Wang
BioMed research international 2015, 2015
Predicting drug response and synergy using a deep learning model of human Cancer cells
BM Kuenzi, J Park, SH Fong, KS Sanchez, J Lee, JF Kreisberg, J Ma, ...
Cancer Cell 38 (5), 672-684. e6, 2020
Classifying tumors by supervised network propagation
W Zhang, J Ma, T Ideker
Bioinformatics 34 (13), i484-i493, 2018
Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome
T Wang, J Ma, AN Hogan, S Fong, K Licon, B Tsui, JF Kreisberg, ...
Cell systems 11 (2), 176-185. e6, 2020
Estimating the partition function of graphical models using Langevin importance sampling
J Ma, J Peng, S Wang, J Xu
Artificial Intelligence and Statistics, 433-441, 2013
Typing tumors using pathways selected by somatic evolution
S Wang, J Ma, W Zhang, JP Shen, J Huang, J Peng, T Ideker
Nature communications 9 (1), 1-11, 2018
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