Jason Moore

Chair and Professor

Cedars-Sinai Medical Center

United States

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Jason Moore is the Chair and Professor at Cedars-Sinai Medical Center, USA. His research focuses on the integration of artificial intelligence with biomedical knowledge, particularly in areas such as Alzheimer’s disease, feature selection strategies, and advancements in cardiology. He has contributed significantly to the development of knowledge graphs and large language models in healthcare, emphasizing their role in enhancing reasoning and innovation in medical technologies.

Jason Moore

Research Interests

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Articles (16)

A novel computational analysis integrating social determinants information from EHR and literature with Alzheimer’s disease biological knowledge through large language models and knowledge graphs

ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning

Artificial intelligence and technology collaboratories: Empowering innovation in <scp>AI</scp> + <scp>AgeTech</scp>

KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models

Artificial intelligence: revolutionizing cardiology with large language models

A review of feature selection strategies utilizing graph data structures and Knowledge Graphs

Aliro: an automated machine learning tool leveraging large language models

Benchmarking AutoML frameworks for disease prediction using medical claims

Generative and reproducible benchmarks or comprehensive evaluation machine learning classifiers

Predictive value of common genetic variants in idiopathic pulmonary fibrosis survival

The promise of automated machine learning for the genetic analysis of complex traits

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods

TPOT-NN: augmenting tree-based automated machine learning with neural network estimators

REGENS: an open source Python package for simulating realistic autosomal genotypes

Ten simple rules for writing a paper about scientific software

Scaling tree-based automated machine learning to biomedical big data with a feature set selector

Collaborators (8)

George Demiris

H Novais-Bastos1,2

Graciela Gonzalez Hernandez

Folkert Wouter Asselbergs

Jeremy D. Walston

Peter M. Abadir

DUY DUONG-TRAN

Joseph Romano

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