About

Christine Chang, M.D., is an Assistant Professor of Clinical Anesthesiology and Critical Care at the Perelman School of Medicine at the University of Pennsylvania. She completed her undergraduate education at Oberlin College in 2013 and earned her medical degree from the Cleveland Clinic Lerner College of Medicine in 2018. Her professional focus includes anesthesiology and critical care, with research contributions that involve the study of blood pressure perturbations after abdominal surgery, the association between obstructive sleep apnea risk and postoperative respiratory compromise, and the effects of intravenous acetaminophen on postoperative opioid-related complications. Dr. Chang has participated in various research projects and has co-authored publications in these areas, contributing to the advancement of knowledge in anesthesiology and perioperative medicine.

Research topics

• Computer science
• Data mining
• Artificial intelligence
• Machine learning
• Medicine

Selected publications

• Simultaneous Representation Learning of Multi‐Omics and Clinical Outcome Data via a Supervised Knowledge‐Guided Bayesian Factor Model

  Statistics in Medicine · 2026-04-26

  articleOpen access

  With the advent of high-throughput techniques, multi-omics data and various clinical outcomes have been collected for a range of diseases. Multi-omics data play a crucial role in uncovering complex biological processes, yet simultaneous representation learning of such high-dimensional, heterogeneous multi-modality data along with clinical outcomes remains limited. To address this gap, we propose a supervised knowledge-guided Bayesian factor model for integrative analysis of multi-omics and clinical outcome data. The proposed method simultaneously extracts an informative low-dimensional representation and predicts one or more clinical outcomes of interest. The two-level adaptive shrinkage in the novel hierarchical priors allows for the identification of both active modalities and features, resulting in a biologically meaningful structural identification of the high-dimensional data. Moreover, the method is robust to noisy edges in biological graphs that do not align with ground truth. Finally, the proposed method can handle different data types including both continuous and categorical data. Extensive simulation studies and real data analyses of Alzheimer's disease (AD) data demonstrate the advantages of the proposed approach over existing methods. Notably, our analysis of multi-omics and imaging phenotype data from ADNI provides meaningful insights into the underlying biological mechanisms of AD.

  PublisherDOI

• A genetically informed brain atlas for enhancing brain imaging genomics

  Nature Communications · 2025-04-14 · 4 citations

  articleOpen access

  Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

  PublisherOA PDFDOI

• Longitudinal plasma proteomics: relation to incident Alzheimer's disease dementia and biomarkers

  Alzheimer s & Dementia · 2025-11-01

  articleOpen access

  INTRODUCTION: We investigated whether longitudinal changes in plasma proteins were associated with baseline cognitive stages related to Alzheimer's disease (AD), their progression, and AD biomarkers. METHODS: We analyzed longitudinal proteomics (SomaScan 7K) data (N = 347) from the Indiana AD Research Center using linear mixed-effects models for associations with baseline cognitive stages, AD dementia (ADD) conversion, and AD imaging/plasma biomarkers, followed by machine learning analysis to evaluate predictive performance for incident ADD. RESULTS: Our analysis identified two proteins (ACES and IGFALS) associated with baseline diagnosis stages and six proteins (ACES, C7, ZCD1, IL-17C, CC055, and SO5A1) associated with incident ADD. Longitudinal changes of the identified proteins were also associated with AD imaging/plasma biomarkers. The inclusion of longitudinal protein changes yielded an AUC of 84.8% for predicting incident ADD. CONCLUSION: Our findings showed molecular signatures for AD progression and the potential of dynamic changes in plasma proteins as biomarkers for predicting incident ADD. HIGHLIGHTS: Changes in plasma ACES and IGFALS linked to baseline AD cognitive stages Changes in ACES, C7, ZCD1, IL-17C, CC055, and SO5A1 associated with incident ADD Changes in those proteins correlated with baseline AD imaging and plasma biomarkers Proteomics model achieved 84.8% AUC-ROC in predicting incident ADD.

  PublisherOA PDFDOI

• Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data

  Biometrics · 2024-01-29 · 4 citations

  articleOpen access

  There is a growing body of literature on knowledge-guided statistical learning methods for analysis of structured high-dimensional data (such as genomic and transcriptomic data) that can incorporate knowledge of underlying networks derived from functional genomics and functional proteomics. These methods have been shown to improve variable selection and prediction accuracy and yield more interpretable results. However, these methods typically use graphs extracted from existing databases or rely on subject matter expertise, which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian modeling framework to account for network noise in regression models involving structured high-dimensional predictors. Specifically, we use 2 sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed predictors in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian regression model with structured high-dimensional predictors involving an adaptive structured shrinkage prior. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of a genomics dataset and another proteomics dataset for Alzheimer's disease.

  PublisherOA PDFDOI

• Circulating KRAS G12D but not G12V is associated with survival in metastatic pancreatic ductal adenocarcinoma

  Nature Communications · 2024-07-09 · 10 citations

  articleOpen access

  While high circulating tumor DNA (ctDNA) levels are associated with poor survival for multiple cancers, variant-specific differences in the association of ctDNA levels and survival have not been examined. Here we investigate KRAS ctDNA (ctKRAS) variant-specific associations with overall and progression-free survival (OS/PFS) in first-line metastatic pancreatic ductal adenocarcinoma (mPDAC) for patients receiving chemoimmunotherapy ("PRINCE", NCT03214250), and an independent cohort receiving standard of care (SOC) chemotherapy. For PRINCE, higher baseline plasma levels are associated with worse OS for ctKRAS G12D (log-rank p = 0.0010) but not G12V (p = 0.7101), even with adjustment for clinical covariates. Early, on-therapy clearance of G12D (p = 0.0002), but not G12V (p = 0.4058), strongly associates with OS for PRINCE. Similar results are obtained for the SOC cohort, and for PFS in both cohorts. These results suggest ctKRAS G12D but not G12V as a promising prognostic biomarker for mPDAC and that G12D clearance could also serve as an early biomarker of response.

  PublisherOA PDFDOI

• Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors

  Biometrics · 2024-01-29 · 4 citations

  articleOpen accessCorresponding

  There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

  PublisherOA PDFDOI

• A robust penalized-regression-based method for multivariable Mendelian randomization using GWAS summary statistics

  Research Square · 2023-10-03

  preprintOpen access

  Abstract Mendelian randomization (MR) is a statistical approach to inferring the causal relationships from genome-wide association studies (GWAS) by using genetic variants as instrumental variables (IVs). As IVs, the selected genetic variants should be solely associated with the exposure of interest, and have no associations with confounders and the studied outcome except through the exposure. Sometimes the selected genetic variants have effects on the outcome through other pathways, a phenomenon known as horizontal pleiotropy, which makes the selected variants violate IV assumptions. Two different approaches have been proposed to address this issue: one is to improve robustness to pleiotropic effects, and the other one is to generalize univariate exposure to multivariate cases so that we can incorporate possible pathways into MR analysis. Compared to pleiotropy-robust methods, multivariable Mendelian randomization (MVMR) can uncover the exposures having direct effects on the outcome. However, measuring all possible pathways from genetic variants to the outcome in MVMR analysis is difficult. Although MVMR methods with robustness to unmeasured pleiotropy have been proposed recently, they are statistically inefficient as a result of ignoring correlations among exposures and distributions of effect sizes. Given the limitations from both directions, we propose a novel method named MVMR-PRESS that can infer causal relationships between multivariate exposures and the outcome with robustness to horizontal pleiotropic effects from unconsidered pathways. MVMR-PRESS estimates causal effects by using a Penalized Regression on Summary Statistics from GWAS considering both correlations among exposures and distributions of effect sizes. One merit of MVMR-PRESS is that samples in GWAS of different exposures can have overlaps, which allows us to include GWAS summary statistics from the same cohort or consortium. Simulation experiments showed that our method achieved the smallest bias and highest power compared to existing pleiotropy-robust MR and MVMR methods while the type 1 error rate was well-controlled. Applying MVMR-PRESS to publicly available GWAS summary statistics demonstrated that body mass index (BMI), height (HT), and low-density lipoprotein cholesterol (LDL) have significant causal effects on coronary artery disease (CAD), and BMI and high-density lipoprotein cholesterol (HDL) were causally related to type 2 diabetes (T2D). On the contrary, the causal estimates of triglycerides (TG), HDL, and total cholesterol (TC) on CAD, and the estimates of HT, LDL, TG, and TC on T2D were non-significant. In addition, we found no evidence suggesting BMI, HT, and lipid levels have causal effects on inflammatory bowel disease (IBD) and schizophrenia (SCZ).

  PublisherOA PDFDOI

• Robust knowledge-guided biclustering for multi-omics data

  Briefings in Bioinformatics · 2023-11-22 · 4 citations

  articleOpen access

  Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data.

  PublisherOA PDFDOI

• Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis

  Briefings in Bioinformatics · 2023-03-01 · 13 citations

  articleOpen accessCorresponding

  MOTIVATION: With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. METHOD: Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. RESULTS: We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. AVAILABILITY: Code are publicly available at https://github.com/JingxuanBao/SBFA. CONTACT: qlong@upenn.edu.

  PublisherOA PDFDOI

• An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction

  Biostatistics · 2023-07-26

  articleOpen access

  Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction.

  PublisherOA PDFDOI

Frequent coauthors

• Qi Long

  36 shared

• Jingwen Zhang

  12 shared

• Yize Zhao

  Yale University

  10 shared

• Jeong Hoon Jang

  Yonsei University

  7 shared

• Amita K. Manatunga

  Emory University

  6 shared

• Suprateek Kundu

  The University of Texas MD Anderson Cancer Center

  5 shared

• Andrew Taylor

  5 shared

• Qiyiwen Zhang

  University of Pittsburgh

  5 shared

Labs

• Christine Chang LabPI