RAiSD-AI

RAiSD-AI trains, tests, and deploys Convolutional Neural Networks to detect selective sweeps in genomic data, extending the original RAiSD software with machine learning capabilities.

Key Features:

• SNP Data Processing: RAiSD-AI processes SNP data for selective sweep detection.
• CNN Model Training: RAiSD-AI trains Convolutional Neural Networks using TensorFlow or PyTorch.
• Genome-wide Selective Sweep Detection: RAiSD-AI deploys trained CNNs for genome-wide scans to detect selective sweeps.
• Model Testing: RAiSD-AI tests trained Convolutional Neural Networks before deployment.

Scientific Applications:

• Selective Sweep Detection: RAiSD-AI detects selective sweeps in genomic data using deep learning.
• Genomic Scanning: RAiSD-AI performs genomic scans for selective sweeps using deployed CNN models.

Methodology:

RAiSD-AI trains, tests, and deploys Convolutional Neural Networks using TensorFlow or PyTorch for selective sweep detection in SNP data.