A powerful tool for researchers that analyzes complex biomedical data, generating insights and predictive models to accelerate drug discovery.

• Principal Component Analysis (PCA): Reduce dimensionality of complex biomedical data while preserving variance
• Clustering Analysis: Group similar samples to identify natural subgroups in your data
• Predictive Modeling: Build models to predict drug response based on gene expression data

• Python 3.8+
• Flask
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

1. Clone this repository:

   git clone https://github.com/yourusername/bioinsight-analyzer.git
   cd bioinsight-analyzer
   

2. Install required packages:

   pip install -r requirements.txt
   

3. Run the application:

   python app.py
   

4. Open your browser and navigate to:

   http://localhost:8080
   

1. Upload Data: Upload your biomedical data in CSV format or use the provided sample data
2. Select Analysis Type: Choose from PCA, Clustering, or Predictive Modeling
3. Analyze: Click the "Analyze Data" button to process your data
4. View Results: Explore visualizations, statistics, and insights generated from your data

The application includes sample biomedical data that simulates gene expression profiles and drug response measurements. You can use this sample data to explore the features of BioInsight Analyzer.

This project is licensed under the MIT License - see the LICENSE file for details.