Independent Research Report — Exploring Genetic Disparities and Predictive Modeling
Exploring Genetic Disparities and Predictive Modeling in Gastric Cancer
In this project, I analyzed TCGA genomic data to study genetic differences in stomach cancer between Asian and non-Hispanic White patients. Using statistical tests, I explored mutation patterns in genes such as APC and PRKAA1, and although the sample size was limited, I found potential trends worth further study.
I also built machine learning models (XGBoost and Neural Networks) to predict cancer types from mutation data, achieving up to 81% accuracy. This experience combined my interests in biology, data science, and machine learning, and gave me insight into how computational tools can contribute to medical research
Mentored Research
Reconstructing Chaotic Dynamics with Delay Embedding and Gaussian Process Regression and Applying the framework to ecological biomass data
Independent Research
Exploring Genetic Disparities and Predictive Modeling in Gastric Cancer: A Statistical and Machine Learning Approach Using TCGA Data
