AD-scRNA2QSAR: Bridging Genomics and Cheminformatics for Alzheimer's Research 🧠🔬

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Table of Contents

1. Overview
2. Features
3. Installation
4. Usage
5. Pipeline Workflow
6. Data Requirements
7. Model Training
8. Results Interpretation
9. Contributing
10. License
11. Contact

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Overview

AD-scRNA2QSAR is a comprehensive computational pipeline designed to enhance Alzheimer's Disease research. This project integrates single-cell RNA sequencing (scRNA-seq) data with cheminformatics techniques. The goal is to create predictive models for drug discovery, facilitating the transition from raw data to actionable insights.

The pipeline leverages advanced bioinformatics and machine learning methods. It streamlines the workflow, making it accessible for researchers. You can download the latest release here.

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Features

• Seamless Integration: Combines scRNA-seq and cheminformatics.
• Predictive Modeling: Develops QSAR models for drug discovery.
• User-Friendly Interface: Built with Flask for easy interaction.
• Extensive Documentation: Guides users through every step.
• Open Source: Contributions are welcome.

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Installation

To get started, follow these steps to install AD-scRNA2QSAR:

1. Clone the Repository:

   git clone https://github.com/Dazai210/AD-scRNA2QSAR.git
   cd AD-scRNA2QSAR

2. Install Dependencies: Use pip to install the required packages.

   pip install -r requirements.txt

3. Download Release: Download the latest release from the Releases section. Ensure to execute the necessary files.

4. Set Up Environment: Configure your environment variables as needed for your system.

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Usage

After installation, you can start using the pipeline. Run the Flask application with the following command:

python app.py

Access the application in your web browser at http://127.0.0.1:5000.

Uploading Data

1. Navigate to the upload section.
2. Select your scRNA-seq data file.
3. Click "Upload" to begin processing.

Running the Pipeline

Once the data is uploaded, you can initiate the analysis by clicking "Run Pipeline." The system will process the data and provide results in a user-friendly format.

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Pipeline Workflow

The AD-scRNA2QSAR pipeline consists of several key stages:

1. Data Preprocessing:

   • Quality control of scRNA-seq data.
   • Normalization and transformation.

2. Feature Selection:

   • Identify significant genes for analysis.
   • Reduce dimensionality using PCA or t-SNE.

3. Model Training:

   • Train machine learning models on selected features.
   • Use cross-validation for model evaluation.

4. Prediction:

   • Generate predictions for potential drug candidates.
   • Assess model performance using metrics like AUC and accuracy.

5. Results Visualization:

   • Display results using interactive plots.
   • Export findings for further analysis.

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Data Requirements

The pipeline requires specific data formats for optimal performance:

• scRNA-seq Data: Must be in CSV or TXT format, containing gene expression levels.
• Metadata: Include sample information and experimental conditions.
• Chemical Data: For QSAR modeling, provide molecular descriptors in a compatible format.

Example Data Structure

gene_id, sample1, sample2, sample3
geneA, 5.1, 3.2, 4.5
geneB, 2.3, 1.1, 0.9

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Model Training

The pipeline employs various machine learning algorithms for QSAR modeling:

• Random Forest: Good for handling complex interactions.
• Support Vector Machines (SVM): Effective for high-dimensional data.
• Neural Networks: Suitable for capturing non-linear relationships.

Training Process

1. Data Splitting: Divide the dataset into training and test sets.
2. Model Fitting: Train models using the training set.
3. Hyperparameter Tuning: Optimize model parameters for better performance.

Evaluation Metrics

The models are evaluated based on:

• Accuracy: Proportion of correct predictions.
• Precision: Ratio of true positives to total predicted positives.
• Recall: Ratio of true positives to total actual positives.
• F1 Score: Harmonic mean of precision and recall.

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Results Interpretation

After running the pipeline, users can interpret results through:

• Heatmaps: Visualize gene expression patterns.
• ROC Curves: Assess model performance.
• Feature Importance: Identify key genes influencing predictions.

Exporting Results

Users can export results in various formats (CSV, PDF) for reporting and further analysis.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-branch).
5. Create a pull request.

Please ensure that your code adheres to the project's coding standards and includes appropriate tests.

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License

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

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Contact

For questions or feedback, reach out via:

• GitHub Issues: AD-scRNA2QSAR Issues
• Email: your-email@example.com

Explore the latest release here to start your journey in Alzheimer’s Disease research.