Pan-cancer analysis involves assessing frequently mutated genes and other genomic abnormalities common to many different cancers, regardless of tumor origin. Using next-generation sequencing (NGS), pan-tumor projects such as The Cancer Genome Atlas2 have made significant contributions to our understanding of DNA and RNA variants across many cancer types.
Microscopic view of pan cancer cells: highlighting the diverse morphology of cancerous tissues
The objective of this project is to utilize regression techniques to predict a continuous value using data from The Cancer Genome Atlas (TCGA) Pan-Cancer analysis project.
The goal of this project is to train regression models using this data to predict a continuous value representing a specific molecular characteristic of cancers. The aims are the following:
- Study the genomes of various cancers to better understand their molecular characteristics.
- Assist in developing new treatment strategies
The data, collected from different types of tumors, can be downloaded from the two links below:
This project implements four different regression models using cancer genome data:
- Simple Linear Regression (SLR)
- Multiple Linear Regression (MLR)
- Ridge Regression (RR)
- Lasso Regression (LR)
The models are evaluated using two different metrics: mean squared error (MSE) and cross-validation.
| Model | Score | Cross-validation | Mean Squared Error |
|---|---|---|---|
| Simple Linear Regression | 0.98 | 0.98 | 0.05 |
| Multiple Linear Regression | 0.98 | 0.98 | 0.04 |
| Ridge Regression | 0.97 | 0.96 | 0.05 |
| Lasso Regression | 0.92 | 0.94 | 0.07 |
- Simple and multiple linear regression models exhibit comparable performance, while Ridge and Lasso regression models have slightly lower performance.