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A combined AI approach to biomedical data analysis: Knowledge representation reasoning, machine learning and explainable AI
Presenter:

Satya S. Sahoo, PhD, FAMIA

Date:

11-17-2022

Time:

3:30pm-4:30pm

Location:

Zoom

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

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MUIDSI Dissertation Defense: IDENTIFICATION OF IMMUNE-RELATED GENE SIGNATURES TO EVALUATE IMMUNOTHARAPEUTIC RESPONSE IN CANCER PATIENTS USING EXPLORATORY SUBGROUP DISCOVERY
Presenter:

Olha Kholod

Date:

11-17-2022

Time:

1:00PM-2:00PM

Location:

Zoom

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments

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Exploratory analysis of the use of Telemedicine in Primary care
Presenter:

Deepika Gupta

Date:

11-11-2022

Time:

4:00PM-4:30PM

Location:

2501 Student Center

This research is primarily focused on use of Telemedicine in Primary care and how that usage changed over time especially COVID 19. In this research, we did a scoping review to see how Primary care adapted Telemedicine during COVID-19 and what are some of the successes or challenges with the adaptation. In this research, we

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Identifying Gene-Gene Interactions Protective Against Autism Using Contrast Mining
Presenter:

William Baskett

Date:

11-10-2022

Time:

3:30PM-4:00PM

Location:

2501 Leadership Auditorium

Many genetic variants have been linked with the development of ASD. ASD is also known to be more prevalent in males than in females. The underlying mechanism for this difference is unclear. The polygenic nature of the genetic component of ASD makes studying potential mechanisms difficult if the significance of variants is assessed independently, as

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Early Warning of Health Changes for Older Adults: Implementing a Gaussian Mixture Components Clustering Algorithm to Detect Outliers in Daily Multi-feature Sensor Data Streams
Presenter:

Noah Marchal

Date:

10-27-2022

Time:

4:00PM-4:30PM

Location:

2501 Student Center

In this case study, we evaluate the implementation of Sequential Possibilistic Gaussian Mixture Models (SPGMM) for accurately modeling changes in feature streams antecedent to known health events, thereby providing predictive relevance for clinical use, including identifying the preprocessing requirements for streams prior to algorithm input. SPGMM is a change detection algorithm developed for use in

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