W. C. Young

发表

Integration of multiple data sources for gene network inference using genetic perturbation data

William Chad Young, A. Raftery, K. Y. Yeung, 2017, bioRxiv.

Single-cell profiling uncovers a Muc4-expressing metaplastic gastric cell type sustained by Helicobacter pylori-specific inflammation

R. Gottardo, S. Batra, Greg Finak, 2022, bioRxiv.

Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data.

Ka Yee Yeung, Adrian E Raftery, William Chad Young, 2016, The annals of applied statistics.

Integration of multiple data sources for gene network inference using genetic perturbation data

Adrian E. Raftery, Ka Yee Yeung, Ling-Hong Hung, 2017, bioRxiv.

A baseline transcriptional signature associates with clinical malaria risk in RTS,S/AS01-vaccinated African children

Lindsay N. Carpp, K. Stuart, R. Gottardo, 2021, medRxiv.

Bayesian Methods for Inferring Gene Regulatory Networks

William Chad Young, W. C. Young, 2016 .

Model-Based Clustering with Data Correction for Removing Artifacts in Gene Expression Data 1

William Chad Young, W. C. Young, 2016 .

A composite immune signature parallels disease progression across T1D subjects.

P. Linsley, R. Gottardo, M. Hessner, 2019, JCI insight.

Antibody Fc effector functions and IgG3 associate with decreased HIV-1 risk.

Allan C. deCamp, S. Hammer, J. Mascola, 2019, The Journal of clinical investigation.

Comprehensive Data Integration Approach to Assess Immune Responses and Correlates of RTS,S/AS01-Mediated Protection From Malaria Infection in Controlled Human Malaria Infection Trials

Raphael Gottardo, Savita Pahwa, William Chad Young, 2021, Frontiers in Big Data.

Fast Bayesian inference for gene regulatory networks using ScanBMA

Adrian E. Raftery, Ka Yee Yeung, William Chad Young, 2014, BMC Systems Biology.

Detecting and classifying anomalous behavior in spatiotemporal network data ∗

William Chad Young, E. Fox, J. Blumenstock, 2014 .

Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

Adrian E. Raftery, Ka Yee Yeung, William Chad Young, 2018, Statistical modelling.

fastBMA: scalable network inference and transitive reduction

Adrian E. Raftery, Ka Yee Yeung, Ling-Hong Hung, 2017, bioRxiv.

Fast Bayesian inference for gene regulatory networks using ScanBMA

A. Raftery, K. Y. Yeung, W. C. Young, 2014, BMC Systems Biology.

A posterior probability approach for gene regulatory network inference in genetic perturbation data.

Ka Yee Yeung, Adrian E Raftery, William Chad Young, 2016, Mathematical biosciences and engineering : MBE.