Polypilot product mascot

Introducing PolyPilot:

Our AI-Powered Mentorship Program

Start your trial today

Learn More
Go to Polygence Scholars page
Kevin Yang's cover illustration
Polygence Scholar2023
Kevin Yang's profile

Kevin Yang

Class of 2024Rochester Hills, MI



  • "Predicting the Genetic Factors Involved in Nicotine Dependence Susceptibility" with mentor Julian (Apr. 3, 2023)

Kevin's Symposium Presentation

Project Portfolio

Predicting the Genetic Factors Involved in Nicotine Dependence Susceptibility

Started Sept. 16, 2022

Abstract or project description

Nicotine dependence is a big problem among people of all ages, with increasing prevalence due to the introduction of electronic cigarettes. Previously, genome-wide association studies have found correlations between nicotine dependence and genes on chromosomes 8, 9, 15, and 20 (Quach et al. 2020). A better understanding of the genetic basis of nicotine dependence will aid in the identification of dependence-associated risk factors, and the development of smoking cessation drugs. However, it remains unclear whether we can accurately predict whether or not someone is a smoker from gene expression data alone. If nicotine dependence is highly genetic, then we should be able to predict whether someone is a smoker, addicted to nicotine, or has a high susceptibility to becoming nicotine dependent just from their gene expression. To test whether nicotine use can be predicted from gene expression, I will download RNA-sequencing-based gene expression data from the NCBI Gene Expression Omnibus (NCBI GEO) obtained from smokers and non-smokers. Then, I will use tools such as R ggplot to look for correlations between the expression of known nicotine-dependence-associated genes and smoking status. If significant correlations exist, I will attempt to extract features from the data and generate predictive models, using techniques such as logistic regression or support vector machines, of nicotine dependence from gene expression. Accurate prediction of nicotine usage from gene expression would allow medical professionals to easily determine a person’s recent nicotine usage which may interfere with the effects of certain drugs, enabling more targeted, personalized approaches to treating patients who are dependent on nicotine or susceptible to nicotine dependence.