This repository contains the source code, data, and documentation for our company's AI-driven personalized medicine solutions for brain tumor management. Our goal is to revolutionize the diagnosis, treatment, and monitoring of brain tumors by leveraging artificial intelligence and personalized medicine.
Our solution comprises three main components:
- Early Detection Model: AI-driven models for early detection of brain tumors using accessible biomarker data.
- Personalized Treatment Plans: Personalized treatment plans generated by integrating patient data, genomics, and AI predictions.
- Patient Monitoring and Treatment Adjustment: Advanced AI algorithms for monitoring patient progress and adjusting treatment plans as needed.
Current brain tumour diagnosis relies on surgical biopsy and light microscopic examination of tissue, which has several drawbacks. Our goal is to investigate new 'omics' technologies such as genomics, proteomics, and metabolomics for brain tumour diagnosis and improved classification systems.
- Genomics: Involves studying the gene content of tumour cells to predict outcome and individualize treatment.
- Proteomics: Analyzes the protein content of tumour cells to identify biomarker signatures and potential drug targets.
- Metabolomics: Examines the metabolite content of tumour cells for distinct metabolic profiles of different tumour types and normal brain tissue.
- Biomarker signatures improve diagnostic accuracy compared to individual biomarkers.
- Serum biomarkers: Focuses on blood, which is easily accessible and may contain molecules related to disrupted blood-brain barriers in brain tumour patients.
- Cerebrospinal fluid (CSF) biomarkers: Although more invasive, CSF may contain higher levels of potential biomarkers than serum.
- Tissue biomarkers: Can help in classifying tumours, predicting outcomes, and determining optimal treatment.
- Complex data generated from biomarker discovery experiments require specialized pattern recognition software.
- Unsupervised cluster analysis and the training-and-testing set approach are two common methods for developing classifiers.
By leveraging omics technologies, we aim to provide more accurate brain tumour classification systems and improve diagnosis and treatment for patients.
- Python 3.8+
- numpy
- pandas
- scikit-learn
- TensorFlow
- Keras
- Flask
- Clone the repository:
git clone https://github.com/yourcompany/brain-tumor-management.git
cd brain-tumor-management- Set up a virtual environment and activate it:
python -m venv venv
source venv/bin/activate- Install the required projects
pip install -r requirements.txt- Train the early detection model:
python train_early_detection.py- Use the early detection model to predict the presence of brain tumors:
python predict_early_detection.py --input data/sample_input.csv --output results/predictions.csv- Train the personalized treatment model:
python train_personalized_treatment.py- Generate personalized treatment plans:
python predict_personalized_treatment.py --input data/sample_input.csv --output results/treatment_plans.csv- Train the patient monitoring model:
python train_patient_monitoring.py- Monitor patient progress and adjust treatment plans:
python predict_patient_monitoring.py --input data/sample_input.csv --output results/monitoring_results.csvWe welcome contributions from the community. If you'd like to contribute to our project, please submit a pull request or open an issue with your proposed changes or suggestions.
This project is licensed under the MIT License. See the LICENSE file for more information.