AMIA Informatics Summit Best Student Paper Award

By Karly Balslew On March 21st, doctoral student Humayera Islam won the American Medical Informatics Association (AMIA) Student Paper Competition award for the 2022 AMIA Informatics Summit that was held in Chicago. “It’s one of the biggest platforms for informatics people,” Islam said. “It’s very a very diverse and multidisciplinary platform where anybody that works in this area (informatics) can show their work.”  Students from across the country submitted papers for AMIA 2022 Informatics Summit. Submitted papers then went through a peer-review process before judges selected the top five best papers. These top five were chosen to compete in the…

Analysis of polygenic selection in purebred and crossbred pig genomes using Generation Proxy Selection Mapping

Background Artificial selection on quantitative traits using selection indices in commercial livestock breeding populations causes changes in allele frequency over time, termed selection signatures, at causal loci and other surrounding genomic regions.  Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection signature analyses.  Here, we applied Generation Proxy Selection Mapping (GPSM), a genome-wide association analysis of SNP genotype (38,294 to 46,458 SNPs) on birth date, in four pig populations (15,457, 15,772, 16,595 and 8,447 pigs per population) to identify loci responding to artificial selection over a…

Comprehensive Exam: Using subgroup discovery techniques to identify a high-risk group of suicide attempts among people with diabetes

In 2019, 37.3 million Americans had diabetes mellitus, and 1.2 million Americans aged 18 and older had attempted suicide in the past year. According to previous studies, people with type 1 diabetes were three to four times more likely to attempt suicide, and newly diagnosed with type 2 diabetes were two times more likely to attempt suicide when compared with the general population. However, understanding the relationship between suicide attempts and other risk factors for people with diabetes is still lacking. In medical research, the data mining technique has become a promising way to effectively analyze high-dimensional data by extracting…

Detecting formation and growth of refugee / displaced person camps in the Ukraine crisis: Assisting first-phase humanitarian response using satellite imagery

Amid the worst population displacement crisis in Europe since World War 2, governments and international organizations have struggled with the massive task of tracking and providing aid to Ukrainian refugees and internally displaced persons (IDPs). This presentation reviews requirements and explores solutions for AI-assisted monitoring of formation of ad-hoc refugee encampments and temporary/informal settlements in remotely-sensed imagery to support time-critical humanitarian operations. Data and models for binary geospatial prediction of camp location as well as time-series camp expansion will be discussed, as will deep methods for characterizing similarities and differences among detected encampments.

A Computational Respiration Factor to Detect Abnormal Respiratory Patterns Using a Hydraulic Bed Sensor for Older Adults Aging-in-place

Hydraulic bed sensors are efficient non-wearable, passive sensors for unobtrusive and continuous collection of health data for older adults aging-in-place. Continuous collection of data can speak volumes about onset and development of a disease much before it is diagnosed and that is our vision through this work. Hydraulic bed sensor data yields signal from which three major physiological components can be extracted for sleeping individuals, the ballistocardiogram component, the respiration component, and the bed restlessness component. In this work, we focus on the respiration component to detect any abnormal patterns in respiration, more specifically related to Chronic Obstructive Pulmonary Disorder…

Dissertation Defense: DESIGN AND DEVELOPMENT OF GEOSPATIAL ANALYTICAL RESEARCH KNOWLEDGEBASE (GeoARK)

A consistent finding across health, social, business, and environmental literature is that location matters. To conduct impactful research that can be applied to real-world issues and problems, the research must be grounded within the context of the real world in both place and culture. Significant differences exist and can vary across scales from blocks to neighborhoods to regions. The collection, integration, and use of varied data are foundational to addressing the complex questions of today’s health research. To strategically transform this research, a robust integrated data platform is needed. This research presentation focuses on the design, development, implementation, and use…

MUIDSI Online Data Science and Analytics Program Ranked #7 in Fortune Magazine

The MS Data Science and Analytics (DSA) Program of the MU Institute for Data Science and Informatics (MUIDSI) was  recently ranked the 7th best online master’s degree in Data Science Programs in 2022 by Fortune magazine. The rankings were composed of a selectivity score and a demand score. The selectivity score made up 85% of the ranking which assessed undergraduate GPA of incoming students, the average years of work experience and the universities acceptance rate.  The demand score consisted of total enrollment size of the programs and the number of applicants for the most current year.  As data information continues to rapidly…

Harnessing the power of AI to advance knowledge of Type 1 diabetes

An interdisciplinary team of researchers from the University of Missouri, Children’s Mercy Kansas City and Texas Children’s Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to…

Dissertation Defense – UNDERSTANDING GENOME COMPOSITION OF EUSOCIAL HYMENOPTERAN INSECTS

Genome sequencing of the Western honey bee (Apis mellifera), a model for the biology and evolution of eusocial behavior, has revealed unusual genome compositional characteristics, including a low but heterogeneous GC content, bimodal GC content distribution, and a biased tendency of genes to be located in low GC regions. In this dissertation, we sought to determine whether those features are specific to Apis or shared with other insects and the biological meaning of those features. Chapter 1 reviews the major concepts that tie my dissertation research together, highlighting the importance of recombination, GC composition, and their relationship to the evolution of eusociality.