MUIDSI DISSERTATION DEFENSE: Explainable Artificial Intelligence To Stratify Pan-Cancer Patients For Immune Checkpoint Inhibitor Decision Making

Immune checkpoints are a normal part of the immune system. It engages when proteins on the surface of immune cells called T cells recognize and bind to partner proteins on other cells, such as some tumor cells. Immune based therapies such as ICIs work by blocking checkpoint proteins from binding with their partner proteins. This prevents the “off” signal from being sent, allowing the T cells to kill cancer cells. One such drug act against a checkpoint protein called PD-1 or its partner protein PD-L1. Some tumors turn down the T cell response by producing lots of PD-L1. Recent years…

Overhead imagery training data quality control: Methods for deep feature label anomaly detection

Spatial analysis of large remotely-sensed imagery (RSI) training datasets for within-class variation and between-class separability is key to uncovering issues of data diversity and potential bias, not just when vetting datasets for usage, but also during the actual dataset creation stage. Project managers of complex imagery annotation campaigns have a largely unaddressed need for tools that continuously monitor for data labeling anomalies which may be due to human bias or error. This presentation outlines a deep-feature change detection approach using Geospatial Fréchet Distance (GFD) for automatically measuring significant regional changes in image label appearance (i.e., within-class variance). An experimental setup is designed…

Biological pathways as graphs: comparison of select similarity methods

We extracted biomedical pathways from 47 publications related to non-small cell lung cancer (NSCLC) and mergedthem into a Neo4j graph database. With this graph serving as ground truth for comparing to other pathways that were extracted from other publications, we investigated several methods of calculating graph similarity. Unlike ontologies and engineered data sets that have uniform representations of data objects, graphs extracted from unstructured texts haveto be compared as text-described entities first, and by using common graph similarity methods second. In this work, we discuss ways of comparing biological graphs composed of text-described entities, both on the node level and on the graph level. Nodes, their adjacent neighbors and their relationships that contain nominal properties (features) areconverted into relational measures by being compared to their counterparts in another graph, then aggregated into a single measure. Also, a method of searching for similar nodes is described that can be used to locate potential mislabeled twin…

MUIDSI Comprehensive Exam — Measuring Geodiversity in Remotely-Sensed Imagery: Deep Spatial Change Detection Methods for Dataset Bias Mitigation and Visual Landscape Characterization

Amid explosive growth in availability of multimodal remotely sensed imagery (RSI) data from a constellation of overhead sensors, a lack of understanding persists concerning the actual content of these data sources, in particular the nature of spatial variation in the visual and contextual features in the landscape being imaged. Whether described as spatial domain shift, geographic feature variance or simply geodiversity, this gap of knowledge about RSI dataset content comes with important implications.  On one hand, there is a lack of tools to evaluate heterogeneity and representativeness of objects classes found in labeled RSI training datasets, in particular methods for regional…

2022 Mizzou Faculty Alumni Awards – Notable Presence from MUIDSI

The 67th Annual Faculty Alumni Awardees were announced  on July 20, 2022.  Dr. Chi-Ren Shyu, MU IDSI Director; Dr. Lori Popejoy, MU IDSI Core Faculty and Interim Dean of Sinclair School of Nursing; and Dr. Dr. Stevan Whitt, Senior Associate Dean for Clinical Affairs of Medicine and MU IDSI’s long term physician collaborator, were among the six faculty awardees this year.  According to Mizzou Alumni Association, “First celebrated in 1968, the Faculty Alumni Awards highlight the contributions of exceptional individuals to the University’s growth and core mission, through their professional accomplishments, teaching and research excellence or service to the institution.” To…

Dr. Popejoy leads MU Sinclair School of Nursing as Interim Dean

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GIScience as an Interdisciplinary Bridge in Indigenous Health Equity 

GIS and geographic theories can help bridge a crucial gap in interdisciplinary research projects. Geography is uniquely poised to offer critical and practical analytical support, wrangle spatial data and relate them to other datasets, and ground community-based science within the communities it aims to serve. In the context of the Navajo Nation, a key concern is relating potential exposure to environmental contaminants with cultural identity and the social ramifications of resource extraction. Daniel Beene (DaRBeene@salud.unm.edu) is a Ph.D. student in the Department of Geography and Environmental Studies and a trainee with the METALS (Metals Exposure…

Dr. Blake Meyers Elected to the National Academy of Sciences

Dr. Blake Meyers, MUIDSI Informatics PhD program Core Faculty and Professor in the Division of Plant Science and Technology, and jointly appointed as a Principal Investigator and Member of the Donald Danforth Plant Science Center, has been elected as a Member of the US National Academy of Sciences.  https://www.danforthcenter.org/news/blake-meyers-elected-to-national-academy-of-sciences/

Impact of diabetes status and other factors on risk for thrombotic and thromboembolic events: A multicenter, retrospective analysis using the Cerner Real-World DataTM de-identified COVID-19 cohort 

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a proinflammatory condition that can impact the cardiovascular and cerebrovascular systems, thereby increasing risk for thrombotic and thromboembolic events (TTE). However, little is known about the impact of diabetes status on risk for TTEs during SARS-CoV-2 infection. In this US-based, multicenter retrospective cohort study, we analyze the impact of diabetes status (i.e., diabetes present vs. diabetes absent; Type 1 diabetes versus Type 2 diabetes), race and ethnicity, sex, and other factors on risk for TTEs in adults with suspected and confirmed COVID-19 infection. After using multivariate…