Oct. 27, 2021

A COVID-19 Geospatial Population Health Risk Assessment for Missouri

Aim: Design and analyze a multifactor spatial database to assess COVID-19 risk across individual, community, social, infrastructure and culture contexts at the county level to provide quantitative insight and statistical support for potential mitigation areas. Methods: COVID-19 studies require vast amounts of integrated data to create understanding. MU’s Geospatial Analytical Research Knowledgebase (GeoARK) is a spatially integrated database to support spatial and contextual analytics. Utilizing GeoARK we created six distinct risk databases covering individual susceptibility; transmission; socioeconomic; accessibility; health culture; and exposure. Ordinary least squares regression was used to evaluate combinations of explanatory variables. Selected variables within each risk category…

Oct. 27, 2021

SEE-Diabetes, a Patient-Centered Diabetes Self-Management Education and Support for Older Adults: Findings and Information Needs from Patients’ Perspectives

Objectives: This study seeks to understand the information needs of people with diabetes aged 65 and older gathered through survey and focus groups to inform development of an educational decision aid, SEE-Diabetes (Support-Engage-Empower-Diabetes). The SEE-Diabetes aid provides patient-centered self-management education and support for older people with diabetes. Methods: We surveyed 82 University of Missouri Health Care patients with diabetes Thirty seven surveys were completed. The survey collected demographics, diabetes duration, insulin usage, and the accessibility of clinic notes through a patient portal. For focus groups, nine participants aged 65 and over accepted an invitation to participate. We asked participants to evaluate three randomly selected deidentified representative clinic…

Oct. 27, 2021

MUDSI PhD Student Caleb Grohmann Selected for the Future Leaders for Food and Agriculture (FFAR) Fellowship

Congratulations to second-year PhD student Caleb Grohmann, who was one of twenty-six doctoral students in the country to be selected for the Future Leaders for Food and Agriculture (FFAR) Fellowship. Caleb works under the direction of core faculty member Dr. Jared Decker. Read the full article here.

Oct. 19, 2021

P3DB-4.0: a large-scale plant protein phosphorylation database from diverse species to phosphosite conservation

Since the last version (version 3) of the P3DB was published in 2014, we are very encouraged to see that more and more plant scientists have been using our database including reanalysis of our data collections. In this new version (version 4.0), we extensively re-innovated our database to a brand-new platform (https://www.p3db.org) for accommodating much more data sets and new bioinformatic modules, many of which are suggested by our users and collaborators. In detail, we would like to highlight our novel development from the following aspects: (1) The new P3DB altogether harvests ~220,000 phosphosites in ~57,000 phosphoproteins curated across…

Oct. 12, 2021

Predictive Models for Early Detection of Glaucoma using Electronic Health Records

Glaucoma is the second leading cause of irreversible blindness across the world. Around 70 million people have glaucoma, and 4.4 million people are blind due to undiagnosed glaucoma by optic nerve damage worldwide. Studies suggest that early detection of glaucoma is critically essential for the prevention of irreversible blindness. Effective use of electronic health records (EHR) provides data-driven, evidence-based risk factors for glaucoma progression that would also enable applying a practical machine learning (ML) model to predict glaucoma before the onset of clinical symptoms. Predictive models for the early detection of glaucoma are critical steps for subsequent actionable/preventable interventions. We…

Oct. 6, 2021

Analysis of tumor associated macrophages’ heterogeneity in colorectal cancer patients using single-cell RNA-seq data

Colorectal cancer (CRC) is one of the deadliest malignancies worldwide. Though immune checkpoint inhibition has proven effective for a number of other tumors, it offers benefits in only a small group of CRC. In general, heterogenous cell groups in the tumor microenvironment (TME) are considered as the major barrier for unveiling the causes of low immune response. Therefore, deconvolution of cellular components in highly heterogeneous microenvironments is crucial for understanding those mechanisms. Single cell sequencing technology revolutionized TME research enabling profiling cells in high resolution.  We have analyzed scRNA-seq data from 23 CRC patients with pre-treatment primary tumors using Seurat…

Sep. 29, 2021

EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PATIENT STRATIFICATION AND DRUG REPOSITIONING

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI…

Sep. 29, 2021

Scientific illustrations as a source of biological pathway information

The idea of cross-referencing distinct areas of knowledge to glean new insights is not new, but not much work has been done with automatic analysis of different expressions of the same knowledge source, such as a given scientific text and illustrations that accompany it. The NLM grant “Image-guided Biocuration of Disease Pathways From Scientific Literature” attempts to do just that, using state-of-the-art NLP and graph technologies to extract published knowledge and convert it into both machine- and human-readable formats. With thousands of scientific papers being published every month, such a system can significantly improve visibility of new research and speed…

Sep. 29, 2021

Synteny and compositional domains in honey bee genomes

Analysis of synteny, the conservation of gene order between homologs along chromosome segments, is a standard investigative strategy done in many comparative genomic studies to understand genomic conservation and evolution. It allows us to study the evolutionary history between genomes. Conservation of synteny can reflect highly important regions in the genome or critical functional relationships between orthologous genes. Our recent study of genome compositional features of Hymenopteran provided insights into genomic compositional features of Apis and other insects with different levels of eusociality. To further understand the biological meaning of different GC regions within the honey bee genomes, we analyzed…