Kim-Anh Do, Ph.D

Dr. Kim-Anh Do with colleagues at COTS 2026

Dr. Kim-Anh Do receiving IMS Fellowship Certificate at IMS 2026

Our dedicated research team is at the forefront of cutting-edge development in translational data science focusing on

COmputational Statistics, Medicine, Integrative omiC Research (COSMIC).

Our dynamic group provides a wealth of expertise in developing statistical and machine-learning methodologies to address translational data science challenges including the integration and analysis of biological omic data (genomic, transcriptomic, proteomic, metabolomic, microbiome, single cell RNA-sequencing, and spatial transcriptomic), producing computational tools to efficiently apply our statistical methods to large biological data sets, small clinical data sets or large pan-cancer data sets.
Together, we are passionately committed to advancing statistical research and its transformative impact on healthcare. Our collective efforts underscore the significance of rigorous statistical methods in unraveling the complexities of correlated data in biology and medicine, marking them as vital contributors to the ongoing progress of these fields, particularly in personalized medicine and precision oncology.
Our group provides critical collaborative expertise to data scientists, basic and computational biologists, clinicians, bioinformaticians, geneticists, and statisticians:

• Bayesian Methodology
• Graphical and Neural Network Modeling
• Feature Selection of Biomarkers
• Deep Learning Methodology and Causal Models to Maximize Prediction Performance
• Clinical Trial Design
• Survival Modeling

We also design state-of-the-art courses for the education and training of the next generation of computational statisticians and data scientists.

Contact Me

Dr. Kim-Anh Do receiving the 2024 Janet Norwood award for outstanding woman in statistical sciences

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In the News

Kim-Anh Do honored as a 2026 IMS Fellow (opens in a new tab)

Posted 09-03-2026

The designation of IMS Fellow has been a significant honor for 90 years. Each Fellow has demonstrated distinction in research in statistics or probability or has demonstrated leadership that has profoundly influenced the field.

Kim-Anh Do selected to receive the Janet L. Norwood Award for 2024! (opens in a new tab)

Posted 08-13-2024

This award is given annually and recognizes outstanding achievement by a woman in the statistical sciences. Click the link for the award ceremony

Our Groundbreaking Article on Lethal Lung Cancer Biomarkers Published in Cancers Journal's Special Issue! (opens in a new tab)

Posted 05-30-2024

Click on the title to read more about the Validation of a Blood-Based Protein Biomarker Panel for a Risk Assessment of Lethal Lung Cancer in the Physicians’ Health Study

Our Research Study Earns a Place in MD Anderson's 23 Cancer Research Highlights of 2023! (opens in a new tab)

Posted 12-21-2023

Read more about it on the MD Anderson website: Blood test developed at MD Anderson aids in predicting lung cancer mortality risk

Recent Talk

List of Softwares

aPCoA

BAZE

CAMLU

DINGO, iDINGO

GENECLUST

looPA

mediateR

NExUS

PRECISE

ProgPerm

sparseMbClust

Survival Contour Plot

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Selected Published and Impactful Papers

Published 12-1-2025

SMAGS-LASSO: A Novel Feature Selection Method for Sensitivity Maximization in Early Cancer Detection

We developed SMAGS-LASSO, a machine learning algorithm that combines our developed Sensitivity Maximization at a Given Specificity (SMAGS) framework with L1 regularization for feature selection.

(Click on title to read more)

Published 8-6-2025

Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials

In this work, we show via simulation and theory that trial-level estimate uncertainty may bias the results of meta-analytic models towards positive findings of the quality of the surrogate.

(Click on title to read more)

Published on 7-18-2025

Oral microbial signatures of head and neck cancer patients with diverse longitudinal oral mucositis severity patterns

This study aimed to: (1) characterize longitudinal microbial patterns of oral mucositis severity among head and neck cancer patients; (2) determine clinically relevant patient clusters based on oral mucositis severity...

(Click on title to read more)

Published on 7-11-2025

CAT: a conditional association test for microbiome data using a permutation approach

This paper proposes a novel conditional association test, CAT, that can account for other features and phylogenetic relatedness when testing the association between a feature and an outcome.

(Click on title to read more)

Published on 4-26-2025

Grape-Pi: graph-based neural networks for enhanced protein identification in proteomics pipelines

We developed a graph neural network (GNN)-based model, Graph Neural Network using Protein–Protein Interaction for Enhancing Protein Identification (Grape-Pi), which is applicable to all proteomics pipelines.

(Click on title to read more)

Published on 1-7-2025

Causal models and prediction in cell line perturbation experiments

We propose causal structural equations for modeling how perturbations effect cells. From this model, we derive two estimators for predicting responses: a Linear Regression (LR) estimator and a causal structure...

(Click on title to read more)

Published on 05-30-2024

Validation of a Blood-Based Protein Biomarker Panel for a Risk Assessment of Lethal Lung Cancer in the Physicians' Health Study

This study aimed to assess a four-marker protein panel (4MP)’s performance, including the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19, for predicting lung cancer...

(Click on title to read more)

Published on 01-04-2024

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first...

(Click on title to read more)

Accepted on 11-29-2023

Attempts to Understand Oral Mucositis in Head and Neck Cancer Patients through Omics Studies: A Narrative Review

Oral mucositis (OM) is a common and clinically impactful side effect of cytotoxic cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy with...

(Click on title to read more)

Revised on 11-09-2023

Estimating Causal Effects with Hidden Confounding using Instrumental Variables and Environments

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments...

(Click on title to read more)

Published on 09-20-2023

Mortality Benefit of a Blood-Based Biomarker Panel for Lung Cancer on the Basis of the Prostate, Lung, Colorectal, and Ovarian Cohort

To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model on the basis of subject characteristics to identify individuals at high risk...

(Click on title to read more)

Published on 09-19-2023

A blood-based metabolomic signature predictive of risk for pancreatic cancer

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to...

(Click on title to read more)

Published on 09-08-2023

Study finds link between oral microbiome and common side effect in patients with head and neck cancer

Oral mucositis refers to erythematous and ulcerative lesions of the oral mucosa observed in patients with cancer being treated with chemotherapy, and/or with radiation therapy to fields involving the oral...

(Click on title to read more)

Published on 02-05-2023

Performance determinants of unsupervised clustering methods for microbiome data

In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically...

(Click on title to read more)

Posted on 11-1-2022

A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

(Click on title to read more)

Posted on 10-21-2022

A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data

We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training...

(Click on title to read more)

Posted on 10-14-2022

Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer

The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new...

(Click on title to read more)

Posted on 9-21-2022

Transcriptomic Signatures of Hypomethylating Agent Failure in Myelodysplastic Syndromes and Chronic Myelomonocytic Leukemia. Exp Hematol

Hypomethylating agents (HMAs) are the standard of care for myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). HMA treatment failure is a major clinical problem and its mechanisms are poorly...

(Click on title to read more)

Published on 9-9-2022

CAMLU: A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is...

(Click on title to read more)

Posted on 9-5-2022

MDM2 antagonist improves therapeutic activity of azacitidine in myelodysplastic syndromes and chronic myelomonocytic leukemia

Failure of hypomethylation agent (HMA) treatments is an important issue in myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). Recent studies indicated that function of wildtype TP53 positively impacts outcome...

(Click on title to read more)

Published on 8-28-2019

NExUS: Bayesian simultaneous network estimation across unequal sample sizes

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes...

(Click on title to read more)

Published 10-8-2018

PRECISE: Personalized Integrated Network Modeling of the Cancer Proteome Atlas

Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The...

(Click on title to read more)

Published on 7-6-2015

DINGO: differential network analysis in genomics

Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under...

(Click on title to read more)