Scientific Machine Learning

This project used dataset from The Cancer Genome Atlas Network (TCGA) with 462 CRC samples and 33379 gene expression features. We aim to evaluate the effectiveness of specific machine-learning models, namely K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machines (SVM) in predicting MSI status and identify the genes that plays a critical role in enhancing the predictive accuracy of the model for MSI status in colorectal cancer.