CISC 500 (Undergraduate Thesis) - Interpretable Model Analysis For Oncogene Prediction And Contribution Using Gene Expression Data

📘Overview

This project uses deep learning models, including DNNs (Deep Neural Networks), SVMs (Support Vector Machines) and Logistic Regression models, to classify cancer types based on gene expression data. To address the "black-box" nature of these models, we apply explainable AI (XAI) techniques such as SHAP, DeepLIFT, and LIME to interpret which genes most influence predictions. Identified genes are further analyzed for biological significance through GO and KEGG pathway enrichment.