About

Jason Moore, PhD, is an Adjunct Professor of Biostatistics and Epidemiology at the University of Pennsylvania's Perelman School of Medicine. He is affiliated with the Department of Biostatistics and Epidemiology and the Institute for Biomedical Informatics. His research expertise encompasses artificial intelligence, bioinformatics, biomedical informatics, complex adaptive systems, data science, epistasis, genetic architecture, genetic epidemiology, genomics, human genetics, machine learning, network science, precision medicine, systems biology, translational bioinformatics, visualization, and visual analytics. Dr. Moore has contributed to the development of computational frameworks and methodologies for understanding genetic interactions, gene-gene interactions, and genome-wide association studies, with a focus on complex hierarchical genetic interactions and statistical epistasis networks. His work aims to advance the understanding of genetic factors in human disease and improve analytical approaches in genomics and bioinformatics.

Research topics

• Medicine
• Internal medicine
• Political Science
• Computer Science
• Virology
• Data Mining
• Biology
• Artificial Intelligence
• Machine Learning
• Environmental health
• Knowledge management
• Pathology
• Nursing
• Genetics
• Data science
• Medical emergency
• Public relations
• Emergency medicine
• Immunology
• World Wide Web
• Business

Selected publications

• From Disclosure to Substance: The Next Step for AI Transparency in Science

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  articleOpen accessSenior author

  Disclosure + opinion figures — reproduction bundle================================================== Python scripts-------------- generate_opinion_figures.py — Figure 1 (temporal + forest) & Figure 2 (field-specific ORs), PDFs granularity_analysis.py — tier analysis: forest PNG, tier distribution PNG, modeling frame CSV granularity_bootstrap.py — bootstrap Data/Code OR violin PNGs + JSON Data under output/ (bundled)---------------------------- scorecard_2024_5000.csv, scorecard_2025_5000.csv papers_2024_5000.csv, papers_2025_5000.csv granularity_modeling_frame.csv (for bootstrap without re-fetching full text) opinion_figures/field_results.csv (optional snapshot from a prior run of the field models) Setup----- pip install -r requirements.txt Opinion figures (PDF, as in the manuscript)------------------------------------------- cd /path/to/disclosure_granularity_figures python3 generate_opinion_figures.py Writes: output/opinion_figures/figure1_combined.pdf output/opinion_figures/figure2_field_specific.pdf output/opinion_figures/field_results.csv (refit from scorecards + papers) Note: Figure 1 panels (A) bar heights and (B) pooled ORs are the values coded in generate_opinion_figures.py (main-text point estimates). Figure 2 is estimated from the bundled scorecard + papers tables (fields with n ≥ 250). Granularity + bootstrap figures (PNG)------------------------------------- python3 granularity_bootstrap.py # fast if granularity_modeling_frame.csv present python3 granularity_analysis.py # optional: refetch/cache full text, then PNGs Typical outputs: output/granularity_forest_tiers.png output/granularity_tier_distribution.png output/granularity_bootstrap_data_or.png output/granularity_bootstrap_code_or.png Reference PNGs from an earlier run: output/figures_reference/ Environment (optional)---------------------- GRANULARITY_WORKERS=8 GRANULARITY_MAX_N=0 GRANULARITY_BOOTSTRAP_B=2000 GRANULARITY_BOOTSTRAP_STRATIFIED=1

  PublisherDOI

• From Disclosure to Substance: The Next Step for AI Transparency in Science

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  articleOpen accessSenior author

  Disclosure + opinion figures — reproduction bundle================================================== Python scripts-------------- generate_opinion_figures.py — Figure 1 (temporal + forest) & Figure 2 (field-specific ORs), PDFs granularity_analysis.py — tier analysis: forest PNG, tier distribution PNG, modeling frame CSV granularity_bootstrap.py — bootstrap Data/Code OR violin PNGs + JSON Data under output/ (bundled)---------------------------- scorecard_2024_5000.csv, scorecard_2025_5000.csv papers_2024_5000.csv, papers_2025_5000.csv granularity_modeling_frame.csv (for bootstrap without re-fetching full text) opinion_figures/field_results.csv (optional snapshot from a prior run of the field models) Setup----- pip install -r requirements.txt Opinion figures (PDF, as in the manuscript)------------------------------------------- cd /path/to/disclosure_granularity_figures python3 generate_opinion_figures.py Writes: output/opinion_figures/figure1_combined.pdf output/opinion_figures/figure2_field_specific.pdf output/opinion_figures/field_results.csv (refit from scorecards + papers) Note: Figure 1 panels (A) bar heights and (B) pooled ORs are the values coded in generate_opinion_figures.py (main-text point estimates). Figure 2 is estimated from the bundled scorecard + papers tables (fields with n ≥ 250). Granularity + bootstrap figures (PNG)------------------------------------- python3 granularity_bootstrap.py # fast if granularity_modeling_frame.csv present python3 granularity_analysis.py # optional: refetch/cache full text, then PNGs Typical outputs: output/granularity_forest_tiers.png output/granularity_tier_distribution.png output/granularity_bootstrap_data_or.png output/granularity_bootstrap_code_or.png Reference PNGs from an earlier run: output/figures_reference/ Environment (optional)---------------------- GRANULARITY_WORKERS=8 GRANULARITY_MAX_N=0 GRANULARITY_BOOTSTRAP_B=2000 GRANULARITY_BOOTSTRAP_STRATIFIED=1

  PublisherDOI

• Integrative microRNA and transcriptome analysis reveals sex-specific molecular divergence in human bladder cancer

  Biology of Sex Differences · 2026-01-23

  articleOpen access

  BACKGROUND: Bladder cancer affects men and women differently: men are diagnosed more frequently, but women often present with advanced disease and have worse survival. The biological mechanisms underlying these disparities remain unclear. This study aimed to identify sex-specific molecular features and regulatory interactions that shape tumor biology and outcomes. METHODS: We performed an integrative multi-omics analysis combining bulk messenger RNA and microRNA expression, survival modeling, and single-cell transcriptomic profiling. Data were obtained from The Cancer Genome Atlas, Gene Expression Omnibus, and the Genome Sequence Archive. Differential expression analyses were conducted separately in tumors and in normal samples to compare males and females. Experimentally validated microRNA-mRNA target pairs were tested for correlation, and survival associations were evaluated using Kaplan-Meier and Cox models. Single-cell RNA-seq data were analyzed to assess sex-biased expression across tumor and immune cell populations. RESULTS: We identified 48 tumor-specific sex-biased microRNAs and 456 tumor-specific sex-biased genes, the majority located on autosomes rather than sex chromosomes. Correlation analysis revealed 82 experimentally supported, negatively correlated microRNA-mRNA pairs, including 63 discordant pairs with opposite sex-biased expression, suggesting sex-specific regulatory interactions. Several of these features were significantly associated with overall survival in a sex-dependent manner. For example, the male-upregulated microRNA miR-1270 showed repression of the female-biased targets CYP26B1 and FAM180A, both of which were associated with poor survival, highlighting potential prognostic and therapeutic relevance. Single-cell analysis revealed widespread sex-biased expression across epithelial, stromal, and immune cells, with female tumors showing stronger signals in stromal and immune compartments, which may contribute to the more aggressive clinical course observed in females. CONCLUSIONS: Our findings indicate that sex disparities in bladder cancer are largely driven by post-transcriptional regulation of autosomal genes, rather than sex chromosome dosage. By linking sex-biased microRNAs, target genes, and patient survival with cell type-specific expression, this study provides new insight into the biological basis of sex differences in bladder cancer. These results underscore the importance of incorporating sex as a critical variable in biomarker development, therapeutic targeting, and clinical trial design.

  PublisherDOI

• Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework

  Lecture notes in computer science · 2025-09-22

  book-chapterSenior author
  PublisherDOI

• Deep Learning-based Classification of Patients with Postural Orthostatic Tachycardia Syndrome using Wearable ECG and Accelerometer Data

  2025-12-01

  articleOpen access

  Postural Orthostatic Tachycardia Syndrome (POTS) is a chronic autonomic disorder characterized by chronic (> 3 months) orthostatic intolerance and an increase in heart rate (HR) of ≥ 30 beats per minute (bpm) without orthostatic hypotension. Traditional diagnostic approaches, such as the active standing or tilt-table test, are typically conducted under controlled clinical conditions, limiting their ability to capture the natural variability of symptoms and the intricate physiological responses occurring in daily life. These tests may cause patient discomfort, dizziness, nausea, or syncope. Furthermore, they are timeconsuming and cannot be used as a screening tool for POTS. To address these limitations, this study explored wearable devices that continuously collect physiological data-specifically, electrocardiogram (ECG) and accelerometer (ACC)-derived metrics-from POTS patients and healthy controls during routine daily activities. Physiological features around posturechange events identified in the data were processed and used to train and test a baseline deep learning model. The model demonstrated promising performance in accurately differentiating POTS patients from healthy controls in a relatively small cohort (66 from POTS patients and 20 from controls), indicating its potential as a feasibility study for clinical decision support. Future studies involving larger and more diverse samples under varying clinical conditions would be necessary to enhance the robustness and viability of our diagnostic model.

  PublisherDOI

• Federated feature selection with false discovery rate control

  Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2025-12-20

  article

  Abstract Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this paper, we propose Fed-false discovery rate (FDR), a federated feature selection framework that simultaneously identifies important features while controlling the FDR. To ensure privacy preservation and reduce communication costs, the Fed-FDR shares only lower-dimensional coefficient estimates instead of transmitting summary statistics for all features, with the dimensionality shown to be of the same order as the number of relevant features. The coordinating centre then leverages these lower-dimensional coefficient estimates to construct a generalized mirror statistic to identify the important features. The Fed-FDR is robust to the heterogeneity of feature distribution and model parameters, easy to implement, and computationally efficient. We further demonstrate that Fed-FDR effectively controls the FDR while achieving strong statistical power in our simulation studies. The results of the empirical study also demonstrate that the method is both valid and implementation-ready.

  PublisherDOI

• Drug repurposing for Alzheimer’s disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph

  BioData Mining · 2025-08-05 · 1 citations

  articleOpen accessSenior author

  Drug repurposing (DR) offers a promising alternative to the high cost and low success rate of traditional drug development, especially for complex diseases like Alzheimer's disease (AD). This study addressed DR for AD from three key angles: (1) demonstrating how disease-specific knowledge graphs can improve DR performance, (2) evaluating the role of large language models (LLMs) in enhancing the usability and efficiency of these graphs, and (3) assessing whether Graph-of-Thoughts (GoT)-enhanced LLMs, when integrated with AD knowledge graphs, can outperform traditional machine learning and LLM-based approaches. We tested five distinct DR strategies (DR1-DR5) for AD: DR1, a machine learning method using TxGNN; DR2, a machine learning model leveraging the Alzheimer's KnowledgeBase (AlzKB); DR3, an LLM-based chatbot built on AlzKB; DR4, our ESCARGOT framework combining GoT-enhanced LLMs with AlzKB; and DR5, a general reasoning-driven LLM approach. Results showed that AlzKB significantly improved DR outcomes. ESCARGOT further enhanced performance while reducing the need for coding or advanced expertise in knowledge graph analysis. Because the architecture of AlzKB is easily adaptable to other diseases and ESCARGOT can integrate with various knowledge graph platforms, this framework offers a broadly applicable, innovative tool for accelerating drug discovery through repurposing.

  PublisherOA PDFDOI

• From prompt engineering to agent engineering: expanding the AI toolbox with autonomous agentic AI collaborators for biomedical discovery

  BioData Mining · 2025-11-13 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Is AI overhyped?

  Patterns · 2025-11-01

  articleOpen access1st authorCorresponding

  In this People of Data, we asked five researchers, including three members of the journal's advisory board, whether they feel AI technologies are currently overhyped. Their responses reveal both optimism about the future impact of these technologies and serious concerns about overblown expectations and uncritical applications.

  PublisherOA PDFDOI

• DeepFLAIR*: Improving Multiple Sclerosis Diagnostic Imaging Workflow Using Deep Learning

  medRxiv · 2025-11-27

  preprintOpen access

  ABSTRACT Background Magnetic resonance imaging (MRI) plays a central role in diagnosing multiple sclerosis (MS), yet conventional T2-FLAIR imaging provides limited specificity for distinguishing MS lesions from other white matter abnormalities. The Central Vein Sign (CVS) is a sensitive and specific imaging biomarker which was recently included in the 2024 McDonald criteria for MS diagnosis. FLAIR*, which combines T2-FLAIR and T2* 3D EPI acquisitions, provides optimal detection of the CVS; however, this post-processing workflow requires two separate scans which increases scan time, susceptibility to motion artifacts, and registration error, thus limiting clinical deployment. This study aims to address this issue using a novel deep learning methodology called DeepFLAIR*. Methods Retrospective analysis was performed on multicenter 3-Tesla brain MRI data as part of the Central Vein Sign in Multiple Sclerosis (CAVS-MS) study. The dataset included 315 participants scanned on Siemens and Philips 3T systems using standardized protocols incorporating 3D T2-FLAIR and 3D T2*-weighted EPI acquisitions (0.65-mm isotropic resolution; scan times ≈ 6-7 minutes per sequence). A 3D U-Net-based conditional generative model, DeepFLAIR*, was developed to synthesize FLAIR* contrast directly from single-sequence T2* 3D EPI images. The model was trained and validated using 89 subjects and tested on an independent cohort of 226 subjects. Quantitative evaluation included structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (MSE), and contrast-to-noise ratio (CNR) across lesion-vein, lesion-white matter, vein-white matter, and white matter-cerebrospinal fluid regions. Statistical comparisons between real-world and synthetic FLAIR* images were performed using paired Wilcoxon signed-rank tests with false discovery rate correction (α = 0.05). Results Quantitative metrics confirmed that DeepFLAIR* achieved significantly improved contrast-to-noise ratios and comparable global similarity measures relative to real-world FLAIR* (P < 0.001). Synthetic FLAIR* images demonstrated high structural fidelity to real-world FLAIR* (SSIM = 0.78 ± 0.03, PSNR = 23.6 ± 1.35 dB, MSE = 0.0045 ± 0.0015). CNR analyses revealed enhanced lesion-vein and vein-white matter contrast, confirming preservation of perivenular morphology relevant to CVS detection. Lesion morphology and vein-lesion spatial relationships were consistently preserved across subjects. Conclusions This study demonstrates feasibility of our novel DeepFLAIR* methodology for generating diagnostically relevant FLAIR* contrast from a single T2* 3D EPI input, thereby eliminating the need for dual acquisitions and offline post-processing. This approach could streamline MRI workflows, expand clinical access to CVS-based MS evaluation, and facilitate automated biomarker detection in future diagnostic pipelines.

  PublisherOA PDFDOI

Recent grants

• Biomedical Computing and Informatics Strategies for Precision Medicine

  NIH · $1.8M · 2017–2021

• Informatics Algorithms for Genomic Analysis of Brain Imaging Data

  NIH · $1.4M · 2020–2026

• NIH Grant R25CA134286

  NIH · $3.0M · 2020

• NIH Grant R01LM009012

  NIH · $3.3M · 2016

• Penn integrated Human Pancreas procurement and Analysis Program

  NIH · $17.8M · 2016–2021

Frequent coauthors

• Hao Chen

  Jiangsu Province Hospital

  462 shared

• Scott M. Williams

  Case Western Reserve University

  179 shared

• Hao Chen

  University of Macau

  134 shared

• Folkert W. Asselbergs

  University College London

  133 shared

• Andrew J. Saykin

  Indiana University

  123 shared

• Li Shen

  115 shared

• Marylyn D. Ritchie

  University of Pennsylvania

  101 shared

• Ryan J. Urbanowicz

  Cedars-Sinai Medical Center

  85 shared

Labs

• Computational Genetics LaboratoryPI

Education

• PhD, Human Genetics

  University of Michigan

  1999

• MA, Statistics

  University of Michigan

  1998

• MS, Human Genetics

  University of Michigan

  1994

• BS, Biological Sciences

  Florida State University

  1991