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Polygence Scholar2024
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Arushi Singh

Class of 2025United States of America

About

Hello! my name is Arushi Singh, my polygence project is on how computer science

Projects

  • "Making a webapp for visualizing cancer data" with mentor Christopher (Working project)
  • "Predicting breast cancer subtype outcomes and the driver mutations that lead to different subtypes" with mentor Hugh (Jan. 18, 2024)

Project Portfolio

Making a webapp for visualizing cancer data

Started June 25, 2024

Abstract or project description

Arushi will take data from her previous project and create a website to visualize it. She will create visualizations of the survival curves for different patient cohorts. She will make boxplots of gene expression data. She will also allow users to upload their own data. Their gene expression values will be plotted along with the previously collected data, and the results of the model predictions will be shown.

Project Portfolio

Predicting breast cancer subtype outcomes and the driver mutations that lead to different subtypes

Started June 15, 2023

Abstract or project description

Breast cancer is the most common type of cancer among people and demands for accurate predictive models to help improve patient outcomes. As machine learning models have become more advanced than ever, this can be used to our advantage and help save lives. In this research, we answer two questions and utilize machine learning models to predict the outcomes. Firstly, we investigate applying linear and logistic regression models to predict breast cancer patient's survival rate and status, respectively, with infiltrating duct and lobular carcinoma. These models use a dataset that contains data about patients' cancer stages based on the TNM system and this dataset is used to train/predict patients’ survival rate and status. Secondly, we investigate using a K-means clustering model to predict patients’ breast cancer subtypes based on DNA mutations. The model uses a proteome dataset to help train/predict which one of the three cancer subtypes a patient has and which DNA mutation most likely causes that. Our research is aimed to explore the potential of machine learning to help breast cancer patients and develop models to aid doctors in recommending treatments and making well-informed decisions for patients.