This repository contains a Proof of Concept (POC) for [briefly describe your project, e.g., a data processing pipeline, a web application, etc.]. The primary goal of this POC is to explore the feasibility and demonstrate core functionalities that will be part of the finalized version of the project.
Please note that this is a preliminary version of the project. The finalized version will differ significantly in terms of code structure, optimizations, and additional features.
- Developed a comprehensive data processing system to handle large amounts of genomic data.
- Utilized PostgreSQL for optimized data storage and retrieval.
- Containerized architecture using Docker to ensure consistent and reproducible environments.
- Enabled data upload and database management in isolated containers for better stability.
- The Docker setup is designed to be scalable, with Kubernetes or Docker Swarm proposed for future improvements.
- Potential for load balancing and scaling using Kubernetes.
- Applied various machine learning models on genomic data (VCF files) to determine their effectiveness.
- Implemented hyperparameter optimization techniques like grid search and Bayesian optimization for model tuning.
- Evaluated performance on genomic datasets and clinical datasets for model comparison.
- Improved database query performance using indexing, optimized joins, and block-based techniques.
- Compared unoptimized and optimized query execution times.
- Employed Python scripts for data cleaning and uploading.
- Divided datasets into training and testing subsets for accurate performance evaluation.
- Recommendations include advanced imputation methods for missing data, enhancing system scalability, and creating a user-friendly web interface for the model.
- Future development aims for better container orchestration using Kubernetes and potential web-based interfaces for ease of use.
This POC is intended for testing and demonstration purposes only.
For questions or suggestions, please contact [email protected].