UC San Diego

Ulcerative Colitis is a disease characterized by chronic inflammation within the digestive tract of humans. This disease can have everlasting implications on a persons quality of life and can cause patients to make regular visits to the clinic to ensure their health is not deteriorating which typically results in an endoscopy which is invasive, time consuming, and expensive. Here we explore a multi-omic data set using machine learning and statistics in hopes to find biomarkers that may be indicative of histological remission. Exploring multiple different omic data allowed us to take a holistic view at the protein composition of the cohort that was being studies, ultimately deciding to pursue serum proteins for ease of obtaining samples from patients. We see that there are proteins that are a present within the patients serum that need to be further studied due to their relation to an immune response indicative of processes relating to remission.