TEACHING FACULTY POSITION OPEN – Join an award-winning, on-line, professional master’s program teaching Data Science and Analytics to working professionals from all around the US
The University of Missouri (MU) Informatics Institute is accepting applications for position of Assistant or Associate Teaching Professor of the Data Science and Analytics (DSA) program. We are looking for data scientists with rich industry experience to work in a vibrant environment to educate working professionals to enter the data science workforce. In today’s information-centric world, data are becoming increasingly important for the success of businesses in every industry. The demand for data scientist in the American workforce is expected to continue to grow at a healthy pace for the next ten years and so is the MU Data Science…
Climate-driven urban heat and its adaptation at a large scale
Abstract Among many globally recognized environmental problems such as water scarcity, air pollution, and energy security, heat stress is one of the most severe climate-driven threats to the human society. The situation is further exacerbated in urban areas by urban heat islands (UHIs). Absent measures to ameliorate them, the problems associated with heat stress are expected to intensify due to rapid urban development coupled with climate change. One significant barrier to heat mitigation through urban engineering is the lack of quantitative attribution of the various surface processes toUHI intensity. In this seminar, the intrinsic mechanism of UHI and its quantitative…
MUII Core Faculty Lead the Way in Comparative Oncology
Dr. Jeff Bryan, an MUII core faculty member from the College of Veterinary Science, presented preliminary research at the Veterinary Cancer Society Annual Conference in Louisville, KY on his recent research on a canine bone cancer vaccine that could have promising benefits for humans. Click on the link to learn more.
DATA MINING FOR GENETIC CONTRIBUTIONS TO THE ETIOLOGY OF AUTISM SUBGROUPS
Autism is a collection complex neurological disorders characterized by behavioral, social, and cognitive deficits. Previous investigation of the etiology of autism reveals it to be a complex disorder with no simple way to identify its root cause in most affected individuals. The difficulty determining causal variation leads to the hypothesis that multiple genetic risk factors are necessary in combination to produce the autistic phenotype. Furthermore, the immense phenotypic heterogeneity seen in autism patients leads to a second hypothesis that there exist multiple subtypes of autism with distinct genetic etiologies. We developed new methods combining strategies from bioinformatics, data science, and…
What Can We Learn from a Hundred Thousand E. coli Genomes?
Computational prediction of ubiquitination proteins using evolutionary profiles and functional domains
Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis and localization. Identification of ubiquitination proteins is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well studied model organisms. To reduce experimental costs, computational methods have been introduced to predict ubiquitination sites, but the accuracy is unsatisfactory. If we can predict whether a protein can be ubiquitinated or not, it is meaningful by itself and helpful for…
Information Retrieval from the Electronic Health Records for Patient Cohort Discovery
The widespread adoption of electronic health records has made patient data for re-use. One use case for such re-use is the ability to identify patient cohorts for recruitment into clinical research studies. This talk will describe the use of information retrieval techniques and their evaluation for patient cohort discovery as well as challenges to research involving patient data. William Hersh, MD, FACMI, FAMIA, FACP is Professor and Chair of the Department of Medical Informatics & Clinical Epidemiology in the School of Medicine at Oregon Health & Science University (OHSU) in Portland, Oregon, USA. Dr. Hersh is a leader and innovator in biomedical informatics…
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…
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…