Computational Biology Faculty Research Awards, Fall 2023

Predicting Crohn's Disease Recurrence in Post-Operative Patients through RNA Splicing Profiling and Splicing QTL Analysis

Introduction:
Crohn’s disease (CD) is an inflammatory bowel disease that affects the gastrointestinal tract. Most patients with Crohn’s disease take drugs such as anti-TNF agents and thiopurines to alleviate their symptoms. Patients who are less responsive to their initial treatment or with bowel obstruction may undergo surgery with careful medical assessment. However, surgery does not provide a cure for Crohn’s Disease and the risk of postoperative recurrence is 44-55% after 10 years1.

Using Clinical Data and Waveform Data to Assess Patients in the ICU

Background and Question
Sepsis is a life-threatening condition due to the body’s extreme reaction to an infection.
It is a cascade of immunological reactions triggered by an initial infection. The primary
organs where sepsis attacks originate are the lungs, urinary tract, and gastrointestinal
tract. If sepsis is not treated within an advisable duration it can lead to tissue damage,
organ failure, and even death [1]. The number of sepsis occurrences is steadily
increasing (close to 200,000 yearly in the US). Sepsis occurs when biological/

Characterization of Viral Populations Associated to Salt Marsh Cordgrass Spartina Alterniflora

Background:
Salt marshes are areas of intertidal grasslands. They are important for coast maintenance, erosion control,
carbon regulation, and they supply raw materials and food. One plant that dominates the North American
wetlands is Spartina alterniflora. This smooth cordgrass plays a large role in wetland maintenance through soil stabilization and nutrient recycling [1]. In salt marshes, there is a gradient in the phenotypic height of S.
alterniflora, wherein the plants located closer to the ocean are taller while the plants growing closer to the

Developing a Predictive Modeling Framework for Mental Health Outcomes

BACKGROUND AND QUESTION
Mental health disorders pose a significant challenge to public health and individual well-being. Early identification and prediction of mental health outcomes can lead to targeted interventions and improved treatment outcomes. This research proposal aims to develop a predictive modeling framework for mental health outcomes, schizophrenia, bipolar disorder, anxiety disorder, and major depressive disorder.

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