Rui Jiang
Rui Jiang
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Cited by
Cited by
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Nature Biotechnology 28 (8), 827-838, 2010
Network‐based global inference of human disease genes
X Wu, R Jiang, MQ Zhang, S Li
Molecular Systems Biology 4 (1), 189, 2008
A random forest approach to the detection of epistatic interactions in case-control studies
R Jiang, W Tang, X Wu, W Fu
BMC Bioinformatics 10 (1), 1-12, 2009
Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering
X Hao, R Jiang, T Chen
Bioinformatics 27 (5), 611-618, 2011
Modeling gene regulation from paired expression and chromatin accessibility data
Z Duren, X Chen, R Jiang, Y Wang, WH Wong
Proceedings of the National Academy of Sciences 114 (25), E4914-E4923, 2017
Research in computational molecular biology
J Huang, Q Morris, B Frey
Detecting microRNA targets by linking sequence, microRNA and gene expression …, 2006
Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome
B Jiang, X Liang, Y Chen, T Ma, L Liu, J Li, R Jiang, T Chen, X Zhang, S Li
Scientific Reports 2 (1), 1-15, 2012
From ontology to semantic similarity: calculation of ontology-based semantic similarity
M Gan, X Dou, R Jiang
The Scientific World Journal 2013, 2013
Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy
W Tang, X Wu, R Jiang, Y Li
PLoS Genetics 5 (5), e1000464, 2009
Predicting enhancers with deep convolutional neural networks
X Min, W Zeng, S Chen, N Chen, T Chen, R Jiang
BMC Bioinformatics 18 (13), 35-46, 2017
DeepCDR: a hybrid graph convolutional network for predicting cancer drug response
Q Liu, Z Hu, R Jiang, M Zhou
Bioinformatics 36 (Supplement_2), i911-i918, 2020
Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
Z Liu, H Lou, K Xie, H Wang, N Chen, OM Aparicio, MQ Zhang, R Jiang, ...
Nature Communications 8 (1), 1-9, 2017
Align human interactome with phenome to identify causative genes and networks underlying disease families
X Wu, Q Liu, R Jiang
Bioinformatics 25 (1), 98-104, 2009
Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding
X Min, W Zeng, N Chen, T Chen, R Jiang
Bioinformatics 33 (14), i92-i101, 2017
DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning
W Li, WH Wong, R Jiang
Nucleic Acids Research 47 (10), e60-e60, 2019
Uncover disease genes by maximizing information flow in the phenome–interactome network
Y Chen, T Jiang, R Jiang
Bioinformatics 27 (13), i167-i176, 2011
Constructing a gene semantic similarity network for the inference of disease genes
R Jiang, M Gan, P He
BMC Systems Biology 5 (2), 1-11, 2011
Density estimation using deep generative neural networks
Q Liu, J Xu, R Jiang, WH Wong
Proceedings of the National Academy of Sciences 118 (15), e2101344118, 2021
Prediction of enhancer-promoter interactions via natural language processing
W Zeng, M Wu, R Jiang
BMC Genomics 19 (2), 13-22, 2018
Prediction of deleterious nonsynonymous single-nucleotide polymorphism for human diseases
J Wu, R Jiang
The Scientific World Journal 2013, 2013
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