Genetic targets for autism spectrum disorder identified by MU team
February 22, 2018
COLUMBIA, Mo. – Autism is a spectrum of closely related symptoms involving behavioral, social and cognitive deficits. Early detection of autism in children is key to producing the best outcomes; however, searching for the genetic causes of autism is complicated by various symptoms found within the spectrum. Now, a multi-disciplinary team of researchers at the […]
An RNAmazing research breakthrough
December 19, 2017
Professor of Bioengineering and the Dalton Cardiovascular Research Center Li-Qun (Andrew) Gu and Shi-Jie Chen, joint Professor of Physics, Biochemistry and the MU Informatics Institute and their team recently published “Nanopore electric snapshots of an RNA tertiary folding pathway,” in the prestigious journal Nature Communications.
Dr. Gregory Alexander recently awarded AHRQ grant.
October 9, 2017
Dr. Gregory Alexander, from The Sinclair School of Nursing, was recently awarded a $1,995,522.00 AHRQ grant. This grant will support an interdisciplinary research team who are already contributing to clinical research in long-term care settings. The PI is a doctorally-prepared RN and fellow in the American Academy of Nursing with over two decades of work […]
Leadership Auditorium, 2501 Student Center
USING BIG DATA TO GENERATE HYPOTHESES ON RISK FACTORS FOR POORLY UNDERSTOOD CANCERS
Cancer is one of the most common and deadly diseases and its incidence is increasing. Considering that only 5 -10% of cancers are due to genetics, most cancer types are due to external risk factors such as lifestyle habits and environmental exposure.According to the American Institute for Cancer Research (AICR), 40 percent of cancer cases are preventable through reducing exposure to the controllable risk factors. This means that there are many preventable cancers without prevention recommendations. In order to identify risk factors, innovations in the techniques used to identify risk factors are needed. We will attempt to generate hypotheses about risk factors for cancers based on clinical features, behavioural risk factors, socio-demographics, and exposure or proximity to environmental hazards. This will be done through combining data on cancer incidence and the possible risk factors. The resulting dataset will be analyzed using statistical analyses, GIS analyses, and exploratory data mining.
Dr. Jessica TenenbaumDate:
Jesse Wrench Auditorium, Memorial Union
Health 3.0: Enabling precision medicine through translational bioinformatics and the learning health system
A confluence of technological, computational and legislative advances have put us on the horizon of an exciting time in biomedical research and healthcare, with increasingly blurred boundaries between the two. Advances in experimental technologies enable observation across tens of thousands of molecules at a time. Pervasive mobile devices and an ever-expanding landscape of activity and health-related apps are generating terabytes of data outside of traditional clinical care providers. Advances in computational power and parallel computing facilitate the analysis and interpretation of these diverse streams of data. And an evolving legislative landscape has led to the rapid uptake of electronic health record (EHR) technology nation-wide, as well as complex and ever-changing laws regarding how EHR data may be used and exchanged for clinical care, quality improvement, and secondary research. This talk will describe key advances in these areas, examples of how some of these advances have affected clinical guidelines and actual patients. It will also describe early work in mining EHR data to enable a precision medicine approach to mental illness. The speaker will conclude by discussing how these advances are helping to realize the vision of precision medicine- redefining disease to enable the right intervention for the right person at the right time.
Bio Dr. Tenenbaum is a faculty member in the Division of Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics at Duke University . Her primary research interests are 1. Informatics to enable precision medicine; 2. Mental health informatics; 3. Infrastructure and standards to enable research collaboration and integrative data analysis; and 4. Ethical, legal, and social issues that arise in translational research, direct to consumer genetic testing, and data sharing. Current research projects focus on analyzing electronic health record data to better understand mental illness, target resource allocation, guide treatment, and develop targeted therapeutic interventions.
Nationally, Dr. Tenenbaum plays a leadership role in the American Medical Informatics Association, serving as Chair of the Mental Health Informatics Working Group and as an elected member of the Board of Directors. She is an Associate Editor for the Journal of Biomedical Informatics and served on the advisory panel for Nature Publishing Group’s Scientific Data initiative. After earning her bachelor’s degree in biology from Harvard, Dr. Tenenbaum worked as a program manager at Microsoft Corporation in Redmond, WA for six years before pursuing a PhD in biomedical informatics at Stanford University.
Leadership Auditorium, 2501 Student Center
A Data Analytics Framework for Improving the Efficiency of Stroke Imaging Investigations
Emergency departments are under tremendous pressure to provide high-quality care in the shortest amount of time possible. While not all cases seen in the ED are urgent in nature, some require immediate attention. These true emergencies are usually complex in nature and depend on people, processes and technologies to work seamlessly, in perfect orchestration, in order to achieve the desired outcomes for the patient. One such condition is the stroke, a condition which left untreated (or treated incorrectly) can lead to devastating debilities and even death. To treat stroke successfully, a correct imaging diagnostic needs to be placed and treatment initiated within 3 hours of symptoms onset. Few other conditions in medicine have more stringent time requirements and fewer depend more heavily on timely imaging results for treatment decisions than stroke. Current guidelines mandate that a radiology report for suspicion of stroke be available within 25 to 30 minutes of patient arrival at the institution. As a stroke center of excellence, MU aims to consistently meet these guidelines. However, the complexities of the condition itself and of the system of care suggest that variation will occur. The purpose of our project is to assess the suitability of informatics tools like process mining and statistical process control to study the efficiency of imaging in the stroke care, to assess variability and its sources and to facilitate interventions to increase efficiency and reduce variability.