Deep learning scans for selective sweeps using RAiSD-AI

Nikolaos Alachiotis, Prodromos Papadopoulos, Hanqing Zhao, Pavlos Pavlidis

Recent advances in method and software development for selective sweep detection focus on using deep learning to improve detection performance. However, the adoption of deep learning in real-world analyses is slow, hindered by the lack of reusable tools that alleviate the interdisciplinary friction of integrating such methods for practical deployment. This chapter walks the reader through the basics of using RAiSDAI for selective sweep analysis. RAiSD-AI is a recently introduced tool that can train and test Convolutional Neural Networks (CNNs), and subsequently deploy them for genomic scans for selective sweeps.