Research in the Division of Biomedical Statistics and Informatics advances basic science research, clinical trial design and execution, translational science, and population health through a number of specialized focus areas.
Cancer genomics and evolution
A new horizon in the study of cancer is how scientists classify, quantify, and make sense of changing genomic signatures within and across tumors over time. The area of cancer genomics and evolution has the potential to revolutionize the understanding of diseases such as cancer.
Researchers in the Division of Biomedical Statistics and Informatics at Mayo Clinic are interrogating the evolution of genomic signatures through the use of sequencing technologies to identify and distinguish between germline, somatic and mosaic variations. These distinctions enable scientists and clinicians to better understand the progression of any one patient's cancer and evaluate those patterns against cancer progression in other patients to gain deeper holistic knowledge about the disease.
Clinical trial design and analysis
Faculty and staff members in Section of Cancer Center Statistics in the Division of Biomedical Statistics and Informatics are leaders in the oncology scientific community. They develop new approaches to clinical trial design and analysis to address the promise of precision medicine, rare tumor trials, special populations and alternative endpoints.
The clinical trial design and analysis group has expertise in biomarker-based designs including:
- Adaptive phase I designs
- Biomarker-stratified designs
- Efficient phase III trial designs tailored to new therapeutics in cancer
- Enrichment trial designs
- Sequential multiple assignment randomized trial (SMART) designs
Computational and statistical metagenomics
The collection of the microorganisms found in the human body (the human microbiome) plays a vital role in health and disease. With the aid of sequencing technologies, the human microbiome has been studied through metagenomic sequencing, using either a targeted or shotgun approach.
The computation and statistical metagenomics group in the Division of Biomedical Statistics and Informatics develops and applies computational and statistical tools for the analysis of metagenomic sequencing data. This analysis helps researchers understand the role of the human microbiome in disease susceptibility, initiation, progression and response to treatment, and ultimately to integrate the microbiome data into individualized medicine.
The Division of Biomedical Statistics and Informatics at Mayo Clinic utilizes cutting-edge technologies combined with large observational studies to better understand how genetics influence disease within and across populations. Research is aimed at discovering genes related to disease and developing methods to predict who has an increased risk of disease.
The division's studies cover a broad range of diseases and different study designs. Genetic epidemiology research at Mayo Clinic encompasses:
- Occurrence of disease within families
- Occurrence of specific diseases
- New ways to process and analyze genetic sequence data
- Novel statistical methods to address the role of genes and environment on the risk of disease
Advances in the field of genetic epidemiology improve methods of predicting risk factors for and occurrence of disease and lead to better diagnosis and treatment of patients.
Knowledge discovery and data mining
Knowledge discovery and data mining is an interdisciplinary area built on Mayo Clinic Division of Biomedical Statistics and Informatics' faculty and staff expertise in:
- Data visualization
- High-performance computing
- Machine learning
The primary goal of knowledge discovery and data mining is to develop new methodologies for extracting useful knowledge from data. In health care and biomedicine, the rapid growth of data due to the adoption of electronic health records, wearable devices, online health communities and many curated biomedical databases has created an enormous need for knowledge discovery and data mining methodologies.
Mayo Clinic's knowledge discovery and data mining team aims to develop and implement a suite of data-driven analytic techniques for actionable knowledge from complex, heterogeneous biomedical data. The team explores emerging health data types and researches foundational data mining methods on data representation, data integration and novel analytic visualization to answer significant biomedical questions from the real world.
Natural language processing
Free text is preferred in the health care environment because it is a natural and efficient means of transferring ideas between humans, but it poses considerable challenges for searching, summarization, decision support or statistical analysis. The natural language processing group of the Division of Biomedical Statics and Informatics has pioneered the development of open source tools based on natural language processing to extract information from electronic health record sources.
The division's natural language processing group has successfully deployed generic and disease-specific tools to the clinical setting for:
- Cervical cancer screening
- Hypertrophic cardiomyopathy
- Peripheral arterial disease
Ongoing projects include natural language processing for:
- Clinical and translational research
- Cohort identification
- Detection of silent brain infarct
- Surgical complication surveillance in colorectal surgery
Natural language processing researchers at Mayo Clinic promote system development and public dissemination through the Open Health Natural Language Processing (OHNLP) Consortium and focus on translating natural language processing research into practical algorithms and tools for use in patient care.
Sequencing analysis, pharmacogenomics and systems biology
Custom treatment plans built around genetic aberrations may impact patients' protein interactions, cellular behavior, organ function and bodily health. Devising such plans requires a deep understanding of sequence analysis, pharmacogenomics and systems biology. Experts in the Division of Biomedical Statistics and Informatics collaborate with researchers and clinicians to support them in building genetically customized care plans.
Researchers in the division develop bioinformatics methodologies and software to interrogate complex sequencing analysis, pharmacogenomics and systems biology questions, thereby helping push the boundaries of understanding about the genetic drivers behind a wide variety of human diseases, including cancer.
Analysis of survival and other time-to-event data has played a key role in Mayo Clinic research since the clinic's earliest days. Researchers at Mayo have played a pivotal role in the development of survival analysis methodology and software developed as a result of analysis needs. Read more about survival analysis research in the Division of Biomedical Statistics and Informatics at Mayo Clinic.