Feb. 8, 2023
Exploration of Obesity and Multimorbidity at a Single Academic Institution.
In the US, overweight and obese adults account for more than two-thirds of the total population. Obesity has been established as a risk factor in many chronic conditions; and chronic conditions account for seven out of the top 10 leading causes of death and disability in the US. Multimorbidity occurs when a patient has two or more chronic conditions at the same time (without a single predominant condition). Research has established obesity as a risk factor for many chronic conditions; however, little is known about the co-occurrence of these conditions and the role that obesity plays. This ongoing research is…
Feb. 1, 2023
IDSI PhD student Will Baskett crunched EHR data, finding only a few long-haul symptoms directly related to COVID in comparison to generic viral infection
In a new study, a team of University of Missouri researchers made an unexpected discovery: people experiencing long-lasting effects from COVID-19 — known as “long COVID” or post-COVID conditions — are susceptible to developing only seven health symptoms for up to a year following the infection. They are: fast-beating heart, hair loss, fatigue, chest pain, shortness of breath, joint pain and obesity. Read More…
Feb. 1, 2023
Two IDSI Core Faculty Elected as Fellow of AAAS
Dr. Xiu-Feng “Henry” Wan and Dr. Xiaoqin Zou were named as Fellows of the American Association for the Advancement of Science. Read More…
Jan. 20, 2023
Identification of Spatially Variable Genes
Invasive species pose a unique threat to native species and habitat through direct and indirect competition of resources. Management of invasive species depends on precise identification of their current range and knowledge of how they spread. This project will utilize Plantescope Satellite Imagery to identify the presence of Callery Pear, an invasive ornamental tree species in Missouri. The unique phenology of the Callery Pear should allow for precise identification through Random Forest classification of the imagery. After identification, a logistic model will be built to predict the presence or absence of Callery Pear in the landscape based on a variety…
Jan. 20, 2023
Modeling Pyrus calleryana spread in central Missouri using remote sensing and a non-parametric modeling approach
Invasive species pose a unique threat to native species and habitat through direct and indirect competition of resources. Management of invasive species depends on precise identification of their current range and knowledge of how they spread. This project will utilize Plantescope Satellite Imagery to identify the presence of Callery Pear, an invasive ornamental tree species in Missouri. The unique phenology of the Callery Pear should allow for precise identification through Random Forest classification of the imagery. After identification, a logistic model will be built to predict the presence or absence of Callery Pear in the landscape based on a variety…
Jan. 18, 2023
Relationships between mortality and respiratory health, medication usage, water disappearance, and climatic conditions in commercial weaning-to-finishing pigs
Mortality in pigs is of paramount economic importance to United States pig producers. According to data compiled by MetaFarms SMS and Pork Checkoff in 2022, 21.2% of pigs die before reaching slaughter, and this statistic has increased, in general, over the last 5 years. Although only 6.8% of these losses are attributed to pigs in the wean-to-finish production phase, these pigs are more valuable due to added daily feed expenditure. Thus, reduction of weaning-to-finishing pig mortality is necessary to improve sustainability and profitability of current pig production operations. The objective of the current study was to identify health and climatic variables that are…
Dec. 1, 2022
DeepVariant – TrioTrain: Developing a transfer learning protocol using non-human genomes
Genomic data are widely available for investigating phenotypes that impact both human and animal health. Although the investigation of human health often begins with model organisms, genomics technologies and software are often initially developed with only the human genome in mind, severely limiting their comparative applicability. Translational research requires robust systems-focused, or “One Health,” solutions that enable mutual progress across the animal, plant, and human genomics communities. Regardless of species, genomics faces a common challenge: continuous data re-processing due to a rapidly increasing sample size. The Genome Analysis Toolkit (GATK) is currently the preferred method for calling variants with short-read,…
Nov. 29, 2022
MUIDSI DISSERTATION DEFENSE – ACCELERATING DATA-DRIVEN DISCOVERY IN TYPE 1 DIABETES: AN INFORMATICS-BASED APPROACH
Type 1 diabetes (T1D) is a lifelong chronic disease characterized by the absolute or near-absolute loss of insulin. For affected individuals, management of T1D is an unremitting challenge that involves constant blood glucose monitoring and lifelong administration and titration of exogeneous insulin. Unfortunately, findings from decades of research have not yet comprehensively translated into substantially improved health outcomes, suggesting that limitations inherent in the use of small patient samples and traditional analytical methods have curbed discovery of actionable disease insights. Understanding and addressing ongoing worsened health outcomes in T1D – as well as particular vulnerabilities experienced by subgroups of individuals…
Nov. 16, 2022
A combined AI approach to biomedical data analysis: Knowledge representation reasoning, machine learning and explainable AI
In this talk, I will explore if and how two traditionally distinct fields of AI, that is, ontology engineering and machine learning can be combined to improve performance outcomes. Using real world examples from epilepsy neurological disorder, the talk will demonstrate the use of biomedical ontologies in machine learning workflows to address the critical challenge of feature engineering in multi-modal non-numeric phenotype data. Specifically, we will discuss how biomedical ontologies can improve the performance of machine learning models and the runtime performance of machine learning algorithms. Further, the talk will also explore the role of explainable AI in the context of analyzing electronic health…