Jinghua Gu
According to our database1,
Jinghua Gu
authored at least 15 papers
between 2010 and 2019.
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Bibliography
2019
Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing.
Bioinform., 2019
2018
CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data.
Bioinform., 2018
2017
DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency.
Bioinform., 2017
2014
BADGE: A novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.
BMC Bioinform., 2014
A Markov random field-based Bayesian model to identify genes with differential methylation.
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2014
2013
PhD thesis, 2013
mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks.
BMC Syst. Biol., 2013
Reconstructing transcriptional regulatory networks by probabilistic network component analysis.
Proceedings of the ACM Conference on Bioinformatics, 2013
2012
Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.
Bioinform., 2012
Integrative Network Analysis to Identify Aberrant Pathway Networks in Ovarian Cancer.
Proceedings of the Biocomputing 2012: Proceedings of the Pacific Symposium, 2012
Sampling-Based Subnetwork Identification from Microarray Data and Protein-Protein Interaction Network.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012
Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm.
Proceedings of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2012
2011
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
2010
Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic.
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010