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NOBEL PRIZE 2026

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NIHNATIONAL INSTITUTE ON DRUG ABUSE

Data Center for Acute to Chronic Pain Biosignatures

Active
Institution
Johns Hopkins University, MD
Principal investigator
Martin Lindquist
Funding
$696K
Duration
2019–2027
Open portfolio

Moonbase score

Still being scored

Abstract

Project Summary The Acute to Chronic Pain Signatures (A2CPS) study is generating a rich longitudinal dataset that includes clinical, behavioral, biospecimen, and neuroimaging data aimed at identifying biomarkers that predict the transition from acute to chronic pain. While this dataset holds tremendous potential for advancing pain research, its complexity and scale pose substantial barriers to access, especially for researchers without technical or computational expertise. To address this problem, we propose to develop a domain-adapted large language model (LLM) that allows users to query the A2CPS dataset using natural language. This system will be fine- tuned using both structured metadata and unstructured documentation to ensure semantically accurate and interpretable responses. We will build a user-friendly web interface to support dynamic querying, hypothesis generation, and exploratory data analysis, with outputs including summary statistics, data descriptions, and visualizations. Validation will focus on accuracy, usability, and impact on research efficiency, particularly for non- technical users. This work will improve the accessibility and utility of the A2CPS dataset, support more equitable data-driven research, and establish a generalizable framework for LLM integration with other large-scale biomedical data resources.

Research team

  • Martin LindquistPI
  • Tor D. WagerPI

LOADING MOONBASE SCORE

NATIONAL INSTITUTE ON DRUG ABUSEU54Special Emphasis Panel[ZRG1(70)-R]chronic painpaindatasetsummary statisticsacute chronicacutepain researchllm

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