Dabao Zhang

About

Dabao Zhang is a Professor of Epidemiology and Biostatistics at UC Irvine Wen Public Health. His research and scholarship interests include statistical and computational methodology, such as the construction of large causal systems, exploratory analysis and visualization of big data, generalized linear (mixed) models, integrative analysis of big data, meta-analysis, multivariate extreme values, high-dimensional variable selection, supervised dimension reduction, survival analysis, and transfer learning. Additionally, he specializes in statistical genetics and bioinformatics, focusing on causal inference of transcriptome-wide gene regulatory networks, epistatic interactions, genetic heritability, gene-environment interactions, genome-wide association studies, genomic selection, molecular signature identification, integrative omics data analysis, Mendelian randomization, and pan-cancer analysis of gene regulatory networks. Professor Zhang holds a Ph.D. in Statistics from Cornell University, an M.Sc. in Probability & Statistics from Peking University, and a B.Sc. in Mathematical Statistics from Nankai University. His notable contributions include developing exploratory tools for visualizing relational structures among massive variables in big data, creating computational algorithms to infer biological causality between molecular variables and clinical phenotypes, and defining measures to address the explainability of AI models. His work also involves leveraging generative…

Research topics

• Computer Science
• Econometrics
• Economics
• Statistics
• Mathematics
• Finance
• Botany
• Genetics
• Biology

Selected publications

• SIGNET: transcriptome-wide causal inference for gene regulatory networks

  Scientific Reports · 2023-11-08 · 8 citations

  articleOpen accessSenior author

  Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomizat…

  PublisherOA PDFDOI

• Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

  Proceedings of the National Academy of Sciences · 2024-02-15 · 3 citations

  articleOpen accessCorresponding

  Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered p…

  PublisherDOI

• Coefficients of Determination for Mixed-Effects Models

  arXiv (Cornell University) · 2020-07-16 · 3 citations

  preprintOpen access1st authorCorresponding

  The coefficient of determination is well defined for linear models and its extension is long wanted for mixed-effects models. We revisit its extension to define measures for proportions of variation explained by the whole model, fixed effects only, and random effects only. We propose to calculate unexplained variations conditional on individual random and/or fixed effects so as to keep individual heterogeneity brought by available predictors. While naturally defined for linear mixed models, thes…

  PublisherOA PDFDOI

• Exploring Massive Risk Factors of Categorical Outcomes via Supervised Dimension Reduction

  Journal of Data Science · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  We propose to explore high-dimensional data with categorical outcomes by generalizing the penalized orthogonal-components regression method (POCRE), a supervised dimension reduction method initially proposed for high-dimensional linear regression. This generalized POCRE, i.e., gPOCRE, sequentially builds up orthogonal components by selecting predictors which maximally explain the variation of the response variables. Therefore, gPOCRE simultaneously selects significant predictors and reduces dime…

  PublisherOA PDFDOI

• Prediction Interval Transfer Learning for Linear Regression Using an Empirical Bayes Approach

  Stat · 2025-01-16 · 1 citations

  articleOpen accessSenior authorCorresponding

  ABSTRACT Current literature on transfer learning has been focused on improving the predictive performance corresponding to a small dataset by transferring information to it from a larger but possibly biassed dataset. However, the transfer learning methods currently available do not allow the computation of prediction intervals, and hence, one has to rely on using either the small dataset alone or combining it with the possibly biassed dataset to obtain prediction intervals using traditional line…

  PublisherOA PDFDOI

Recent grants

• CAREER: A New Regularization Framework for Identifying Composite Signatures

  NSF · $433k · 2009–2014

• Modeling Homeostasis of Human Blood Metabolites

  NIH · $2.2M · 2020–2025

• Measuring Explained Variation in Survival Analysis

  NIH · $143k · 2019–2024

Frequent coauthors

• Martin T. Wells

  Cornell University

  23 shared

• Min Zhang

  Zhengzhou People's Hospital

  22 shared

• Patricia A. Cassano

  16 shared

• Bruce W. Turnbull

  University of Leeds

  16 shared

• David Sparrow

  VA Boston Healthcare System

  16 shared

• Vitara Pungpapong

  14 shared

• Min Zhang

  Kunming Medical University

  10 shared

• Daniel Raftery

  University of Washington

  9 shared

Awards & honors

• Purdue University College of Science Outstanding Service Awa…
• Purdue University Seed for Success Award 2011, 2020
• National Science Foundation CAREER Award 2009
• Purdue University College of Science Interdisciplinary Award…
• Cornell University Liu Memorial Award 2003

Similar researchers at University of California, Irvine

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