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https://github.com/alecruces/mebmedbio
Utilizing cutting-edge optimization methods to detect anomalies in medical and biological datasets
https://github.com/alecruces/mebmedbio
anomaly-detection biological-data medical-application
Last synced: 9 days ago
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Utilizing cutting-edge optimization methods to detect anomalies in medical and biological datasets
- Host: GitHub
- URL: https://github.com/alecruces/mebmedbio
- Owner: alecruces
- Created: 2024-04-08T18:55:55.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T19:44:28.000Z (about 2 months ago)
- Last Synced: 2024-11-21T20:43:58.825Z (about 1 month ago)
- Topics: anomaly-detection, biological-data, medical-application
- Language: Jupyter Notebook
- Homepage:
- Size: 2.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Minimum Enclosing Ball for Anomaly Detection on Biological Data
> Analyzing biological data for anomaly detection using Minimum Enclosing Ball (MEB) and variations of the Frank-Wolfe algorithm.
## Keywords
Anomaly detection, biological data
---
## Table of Contents
1. [About the Project](#about-the-project)
2. [Key Features](#key-features)
3. [Key Results](#key-results)
4. [Data Overview](#data-overview)
5. [Methodology](#methodology)
6. [Screenshots and Graphs](#screenshots-and-graphs)
7. [Technologies Used](#technologies-used)
8. [Setup & Installation](#setup--installation)
9. [Usage](#usage)
10. [Contributing](#contributing)
11. [License](#license)
12. [Contact](#contact)
---
### About the Project
This project explores the Minimum Enclosing Ball (MEB) problem and its applications in anomaly detection across biological datasets. We implement three Frank-Wolfe algorithm variants: Pairwise Frank-Wolfe, Blended Pairwise Conditional Gradient (BPCG), and a (1+ϵ)-approximation using Away-Steps. These methods are evaluated for computational efficiency, convergence rates, and anomaly detection performance in biological contexts.
### Key Features
- **Anomaly Detection**: Identification of anomalies in biological datasets.
- **Efficient Algorithms**: Implementation of three Frank-Wolfe-based algorithms, each offering unique advantages in convergence and computational performance.
- **Biological Data Application**: Practical application in detecting anomalies in datasets like breast cancer, gene expression, vertebral pathology, and maternity risk.
### Key Results
- **Convergence**: All three algorithms demonstrated linear convergence on the MEB problem.
- **Best Performance**: The (1+ϵ)-approximation (Yildirim 2008) showed superior computational performance, especially on high-dimensional datasets.
- **Recall Metrics**: Focused on recall as the benchmark for anomaly detection, achieving optimal recall values for most datasets.
### Data Overview
This study uses four biological datasets focused on anomaly detection. Links to each dataset:
- **Breast Cancer**: [Wisconsin Diagnostic Breast Cancer Dataset](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic)
- **Breast Cancer Gene Expression**: [Gene Expression Cancer RNA-Seq](https://archive.ics.uci.edu/dataset/401/gene+expression+cancer+rna+seq)
- **Vertebral Column Pathology**: [Vertebral Column Dataset](https://archive.ics.uci.edu/dataset/212/vertebral+column)
- **Maternity Risk**: [Maternity Risk Dataset](https://archive.ics.uci.edu/dataset/863/maternal+health+risk)
### Methodology
We apply three variants of the Frank-Wolfe algorithm to solve the MEB problem:
- **Pairwise Frank-Wolfe**: An efficient, projection-free algorithm.
- **Blended Pairwise Conditional Gradients (BPCG)**: An optimized variant that minimizes swap steps.
- **(1+ϵ)-Approximation with Away-Steps**: An approximation method designed for efficient handling of large datasets.
Each algorithm is evaluated on convergence rates, computational time, and accuracy metrics (primarily recall) in detecting anomalies.
### Screenshots and Graphs
1. **MEB for Maternity Risk dataset**
2. **Computational Time and Iterations (Table)**
Summary table comparing computational time and iterations for each algorithm across datasets.
| Dataset | Algorithm | Time (ms) | Iterations |
|----------------------|-----------|------------|------------|
| Breast Cancer | PFW | 664.50 | 1,484 |
| Breast Cancer | BPCG | 647.54 | 2,998 |
| Breast Cancer | MEB(A) | 49.15 | 44 |
| Gene Expression | PFW | 369,959.76 | 100,000 |
| Gene Expression | BPCG | 397,067.57 | 100,000 |
| Gene Expression | MEB(A) | 2,116.30 | 44 |
### Technologies Used
> 🛠️ Emphasizing the primary tools and libraries utilized.
- : Main programming language.
- **Optimization Algorithms**: Implemented variations of the Frank-Wolfe algorithm.
- **nbviewer Link**: [View the Jupyter Notebook](https://nbviewer.org/github/alecruces/MEBMedBio/blob/main/MEB_BPCG.ipynb)
### Setup & Installation
Clone the repository and install dependencies:
```bash
# Clone the repository
git clone https://github.com/username/MEBMedBio.git
# Navigate to the project directory
cd MEBMedBio
# Install dependencies
pip install -r requirements.txt
```
## Files
* Code: `MEB_BPCG.ipynb`
* Report: `Report.pdf`
* Presentation: `Presentation.pdf`
### Usage
The repository includes the following files:
- **`MEB_BPCG.ipynb`**: Jupyter notebook with the full workflow, from data preprocessing to algorithm implementation and evaluation.
- **`Report.pdf`**: Detailed report on methodology, algorithmic analysis, and findings.
- **`Presentation.pdf`**: Summary presentation of key findings and insights.
To run the project, open `MEB_BPCG.ipynb` in Jupyter Notebook or [view it on nbviewer](https://nbviewer.org/github/alecruces/MEBMedBio/blob/main/MEB_BPCG.ipynb).
### Contributing
Contributions are welcome! Please see the contributing guidelines for more details.