The background of Peter W. Li, Ph.D., covers software and database engineering, genome assembly and annotation, and gene expression and genotyping technologies.
Dr. Li architected some of the major databases used in the early phase of the Human Genome Project at Johns Hopkins University: Genome Database and Online Mendelian Inheritance in Man.
At Celera Genomics, he led numerous teams related to genome assembly, quality assurance, annotation, single-nucleotide polymorphism (SNP) mining, expression integration and multispecies orthology.
In addition, he managed bioinformatics content and processes for Applied Biosystems gene expression and genotyping assays. He also served on the Scientific Advisory Council for Applied Biosystems to promote scientific excellence and training.
Dr. Li's research interests at Mayo Clinic include genomic analysis and system analysis, especially for practical applications in translational and individualized medicine. He is currently focused on data mining across different data types, pathway and network analysis, and supercomputer architectures.
In the database domain, Dr. Li is participating in enterprise data modeling and developing a new database architecture for organizing and translating meta-data and instance data for research. This new database architecture, Temporal Entity-Attribute-Value with Ownership (TEAVO), allows clinician-scientists to build, manage and analyze patient registries without the need for database engineers to design and query the database for them. It enables end-users to create ad-hoc registries with minimal IT support but still allows efficient IT management of registry resources.
Dr. Li and his team have developed a novel data mining methodology, Association Rule Mining, which finds clinically significant complex interactions from heterogeneous high-dimensional data sets. This has been applied to diabetes, Alzheimer's disease and Parkinson's disease.
In addition, Dr. Li is leading the development of a genome assembler for next-generation sequencing data and a just-in-time individualized cohort analysis using a Cray XMT2, a novel supercomputer.
Significance to patient care
Dr. Li's research is enabling the next generation of clinical care:
- Applying genomic data to personalize medicine
- Managing complex patterns of disease comorbidities
- Exploring just-in-time clinical hypothesis testing through efficient data collection, query and analysis