Jason H. Moore
Biochemistry, Genetics and Molecular Biology · The Ohio State University
Publications
1,186
Citations
47,787
Est. group size
—
Recurring co-author estimate
Active years
61
Publishing since 1966
Jason H. Moore works at the intersection of genetics, bioinformatics, and artificial intelligence, developing computational methods such as genetic programming, automated machine learning, and AI 'agent' systems to analyze large-scale biomedical and genomic data. His research applies these tools to problems like disease risk prediction (e.g., Alzheimer's, bladder cancer, arthritis), multi-omics data integration, and questions of transparency and reliability in AI-driven science. Students would likely engage with method development for data analysis alongside applied biomedical case studies.
Publication output has fluctuated over the past decade with a peak in 2020, followed by a generally moderate and somewhat slowing pace in recent years (mean of about 43 publications/year over the last five years).
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Integrative microRNA and transcriptome analysis reveals sex-specific molecular divergence in human bladder cancer
Biology of Sex Differences · 2026
- From Disclosure to Substance: The Next Step for AI Transparency in Science
Zenodo (CERN European Organization for Nuclear Research) · 2026
- From Disclosure to Substance: The Next Step for AI Transparency in Science
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Moving from disclosure to substance in AI transparency
Patterns · 2026
- Agentic GP: A Theoretical Framework for the Development of Genetic Programming Systems via Agentic AI
Genetic and evolutionary computation · 2026
- GP and LLMs for Program Synthesis: No Clear Winners
Genetic and evolutionary computation · 2026
- EcoXAI: Autonomous Agentic Ecosystem for Explainable Artificial Intelligence and Biomedical Discovery
bioRxiv (Cold Spring Harbor Laboratory) · 2026
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation
Journal of the American Medical Informatics Association · 2026
- ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning
Bioinformatics · 2025
- Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies
Cell Genomics · 2025
- Vibe coding: a new paradigm for biomedical software development
BioData Mining · 2025
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning
Patterns · 2025
- T <sub>H</sub> 17 cells converted into exT <sub>H</sub> 17 cells sustain rheumatoid-like IL-17–independent inflammatory arthritis
Science Immunology · 2025
- Integrative multi-omics study identifies sex-specific molecular signatures and immune modulation in bladder cancer
Frontiers in Bioinformatics · 2025
- Integrative multi‐omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction
Alzheimer s & Dementia · 2025
- arXiv (Cornell University)×49
- BioData Mining×48
- Figshare×32
- bioRxiv (Cold Spring Harbor Laboratory)×27
- Lecture notes in computer science×17
- Taeho Jo
Biochemistry, Genetics and Molecular Biology · Indiana University
- Juan Shu
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Bingxin Zhao
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
- Marios Arvanitis
Biochemistry, Genetics and Molecular Biology · The Ohio State University
- Zirui Fan
Biochemistry, Genetics and Molecular Biology · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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