Sep. 2, 2016

Big Data Colloquium Distinguished Speaker – Dr. Philip S. Yu

The problem of big data has become increasingly importance in recent years. On the one hand, big data is an asset that potentially can offer tremendous value or reward to the data owners. On the other hand, it poses tremendous challenges to distil the value out of the big data. The very nature of big data poses challenges not only due to its volume, and velocity of being generated, but also its variety, where variety means the data can be collected from various sources with different formats from structured data to text to network/graph data, etc. In this talk, we…

Sep. 1, 2016

MUII’s Data Science and Analytics Master’s Program to Deliver Cutting-Edge Training to the NGA with $12 Million Federal Contract

The University of Missouri College of Engineering has just been awarded a five-year, $12 million contract to deliver a comprehensive data science education program that will provide cutting-edge analytical training for the NGA workforce and potentially other members of the U.S. Intelligence Community (IC). This new program will address key education and training needs identified by NGA. The program is a collaboration between the MU College of Engineering’s Center for Geospatial Intelligence (CGI) and the MU Informatics Institute’s Data Science and Analytics (DSA) master’s degree program. The newly established effort is part of the NGA College’s Learning Outreach program that partners with qualified…

Aug. 24, 2016

Soybean science blooms with supercomputers

Soybean Knowledge Base (SoyKB) project finds and shares comprehensive genetic and genomic soybean data through support of NSF-sponsored XSEDE high performance computing. SoyKB helps scientists improve soybean traits. XSEDE Stampede supercomputer 370,000 core hour allocation used in resequencing of over 1,000 soybean germplasm lines. XSEDE ECSS established Pegasus workflow that optimized SoyKB for supercomputers. SoyKB migrated workflow to XSEDE Wrangler data intensive supercomputer. http://www.nsf.gov/news/news_summ.jsp?cntn_id=189594&WT.mc_id=USNSF_195&WT.mc_ev=click

July 5, 2016

Families In Rural Areas Using Telemedicine For Psychiatric, Specialty Care

Mirna Becevic, PhD, an assistant research professor of telemedicine at the School of Medicine, recently was featured in an article by Forbes. Becevic led a study that shows that video-based mental health services are bridging the gap by providing care to underserved areas   http://www.forbes.com/sites/janetwburns/2016/07/05/families-in-rural-areas-using-telemedicine-for-psychiatric-specialty-care/#2746f6334df0…

April 29, 2016

MUII Defense Announcement – Andrew Hutson

Background: The percentage of patients with polypharmacy needs is increasing among a growing patient population. As a result, the amount of time health care professionals require to make clinical decisions based on current and past medications is increasing. Health care professionals need methods for increasing the speed of clinical decision making without sacrificing the quality of care.  The goal of this study is to demonstrate how modifying the data visualization for patient medication histories will change decision making speed or efficacy. Methods: We compared two groups across five randomized blocks. Group 1 responded to questions based on the control data…

April 15, 2016

MUII Defense Announcement – Ginger Han

Biomedical image data have been growing quickly in volume, speed, and complexity, and there is an increasing reliance on the analysis of these data. Biomedical scientists are in need of efficient and accurate analyses of large-scale imaging data, as well as innovative retrieval methods for visually similar imagery across a large-scale data collection to assist complex study in biological and medical applications. Moreover, biomedical images rely on increased resolution to capture subtle phenotypes of diseases, but this poses a challenge for clinicians to sift through haystacks of visual cues to make informative diagnoses.  To tackle these challenges, we developed computational…

April 11, 2016

Botanical treatments to inhibit endometriosis: A next generation RNA-seq data analysis

Endometriosis is a benign but complex gynecologic disorder associated with pelvic pain and infertility and is characterized by the implantation of endometrial tissue outside of the uterus. To date, the reason remains unknown, though the immune dysfunction has been implicated. Hormonal interventions are widely used as effective treatment but with undesirable side effects. Botanical treatments can be alternative options with fewer side effects. In this study, we employed RNA-seq technology to assess the effects of two different botanical treatments, quercetin, and elderberry juice over vehicle (control), to inhibit the growth of endometriotic lesions in a mouse model (C57BL/6) of endometriosis.

March 21, 2016

Mining for epigenetic patterns across species

Exponential growth of next-generation sequencing technologies has made the epigenomics analysis a big data science, which poses the challenges to its translation into knowledge. This has led to the emergence of a new field called “Comparative Epigenomics.” Comparative epigenomics has three major directions, namely comparison across species, across time-course of a biological process, and across individuals. In this study we focus on comparing the epi-modifications across species (particularly between Human and Mouse). We compared different epi-genomic factors and where/how they concur or differ among species. We have used histone modification data from various publicly available data sets in Human and…

March 14, 2016

Sequence mining of EMR log data to understand clinician workflow

Modeling clinical workflow is an important pre-cursor to understanding how clinicians interact with an EMR and its features. Common methods of assessing clinical workflow include video recording or direct observations, or through directed user experience testing in a controlled environment. Assessing clinical workflow within an EMR can also be accomplished by analyzing EMR log data, which can provide an unbiased view of EMR use. Using log data to build workflow patterns requires appropriate pre-processing of the log data, and the use of several types of data mining techniques: frequent pattern mining, sequence mining, and sub-graph mining. This talk will…