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Supporting Population Health Outcomes Studies Using a Framework of Social Determinants Linked EHR Data
Presenter:

Md Kamruz Zaman Rana

Date:

10-27-2022

Time:

3:30PM-4:00PM

Location:

2501 Student Center

Population health outcomes research based on social determinants of health (SDoH) needs to link electronic health record (EHR) data with social determinants using Identifiable information (patients’ addresses). The connectivity expects additional computational load, privacy risk, and storage for each research. A Data Lake that facilitates research data can provide a framework for SDoH-connected EHR data

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Alzheimer’s disease mitigation: AI, neuroimaging and gut-brain axis
Presenter:

Ai-Ling Lin, PhD

Date:

10-20-2022

Time:

3:30pm-4:30pm

Location:

2501 Student Center (Leadership Auditorium)

Alzheimer’s disease (AD) is the most common form of dementia and currently there are no effective therapeutics to reverse the course once the clinical symptoms have developed. Early identification of risk factors for AD and effective interventions thereof would be critical to mitigate AD pathological development and prevent the onset of clinical symptoms. In the

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

Yuanyuan "Daisy" Shen

Date:

09-08-2022

Time:

4:30PM-5:30PM

Location:

Zoom

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

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

Aaron Wesley

Date:

09-08-2022

Time:

4:00PM-4:30PM

Location:

2501 Student Center (Leadership Auditorium)

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

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Biological pathways as graphs: comparison of select similarity methods
Presenter:

Misha Kovalenko

Date:

09-08-2022

Time:

3:30PM-4:00PM

Location:

2501 Student Center (Leadership Auditorium)

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

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