This project focuses on the genomic analysis of SARS-CoV-2 (MN908947.3) using deep learning techniques. The primary goal is to analyze the virus's nucleotide composition, GC content, and functional protein sequences. It utilizes Variational Autoencoders (VAE) to cluster protein sequences and uncover structural and functional insights that can aid in the development of vaccines and therapeutic strategies.
- Analysis of SARS-CoV-2 genomic sequence (MN908947.3)
- Nucleotide composition and GC content analysis
- Identification of functional proteins
- Protein classification using deep learning (VAE)
- Visualization of protein clustering and model training progress
- Data Preparation: Encoding nucleotide sequences for deep learning input.
- VAE Model: Used for clustering protein sequences and reducing dimensionality.
- Metrics: Evaluates model performance with accuracy, precision, recall, and F1-score.
The findings from this project aim to provide deeper insights into SARS-CoV-2 genomics, contributing to therapeutic and vaccine development efforts.