Deep learning scans for selective sweeps using RAiSD-AI

This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. 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. https://doi.org/10.32942/X2GW9B Life Sciences Selective Sweep, Deep Learning, Convolutional Neural Network Published: 2026-03-30 15:13 Last Updated: 2026-03-30 15:13 CC BY Attribution 4.0 International Conflict of interest statement: none Data and Code Availability Statement: yes Language: English