DASDC

Github URL: https://github.com/soo‐h/DASweepDetect

Domain Adversarial Sweep Detection and Classification (DASDC) is an advanced approach designed to enhance the identification and classification of selective sweeps, which are crucial for understanding biological evolution and advancing precision medicine and genetic improvement. This method utilizes a domain-adversarial neural network, which integrates adversarial learning modules to effectively align two different domains—training data and actual genomic data. This alignment addresses the common issue of mismatch in deep learning models, significantly boosting the generalization capabilities, prediction robustness, and accuracy of the DASDC method.