About
Preclinical Trial Database
Project Overview
The Preclinical Database is a comprehensive repository of preclinical trial data extracted from published research papers. Our mission is to facilitate the discovery and analysis of preclinical studies, making it easier for researchers to find relevant information about disease models, drug testing, and animal studies.
This database uses advanced natural language processing and machine learning techniques to extract structured data from scientific publications, enabling researchers to search, filter, and analyze preclinical research more efficiently.
Research Team
Ilias Tagkopoulos
Principal Investigator
University of California, Davis
Leading expert in preclinical research with over 15 years of experience in animal models and drug development.
ilias.tagkopoulos@ucdavis.eduDr. Michael Chen
Data Scientist
Institute of Biomedical Research
Specializes in data extraction, NLP, and machine learning applications for biomedical literature analysis.
m.chen@institute.eduDr. Sarah Johnson
Research Coordinator
Center for Preclinical Studies
Coordinates preclinical trial data collection and validation processes.
sarah.j@center.eduDr. Robert Williams
Bioinformatics Specialist
Department of Computational Biology
Develops tools and pipelines for processing and analyzing preclinical research data.
r.williams@dept.eduDr. Emily Davis
Clinical Research Associate
Medical Research Foundation
Focuses on translating preclinical findings into clinical research applications.
emily.davis@foundation.orgMethodology
- •Automated extraction of preclinical data from published research papers using advanced NLP techniques
- •Confidence scoring system to assess the reliability of extracted information
- •Structured data representation for diseases, drugs, animal species, and experimental parameters
- •Continuous validation and quality assurance processes to ensure data accuracy