RAISING

This repository contains the source code, simulation data, and documentation for the RAISING, a two-stage neural network(NN) implementation framework that first performs hyperparameter tuning to devise the best NN architecture and then performs training on the entire data to estimate the feature importance. The method has been published in Nucleic Acids Research (https://doi.org/10.1093/nar/gkae1027). The source code is available on GitHub(https://github.com/Devashish13/RAISING/)

RAISING installation

Create a conda environment to install the RAISING

conda create -n RAISING_env python=3.9
conda activate RAISING_env

Install the package through pypi

pip install RAISING

Install the package through the github repository

pip install git+https://github.com/Devashish13/RAISING.git

RAISING implementation

Please visit the following link for detailed description of RAISING documentation and tutorials

https://devashish13.github.io/RAISING/

Simulated data generated for detecting polygenic adaptation

Simulated data generated in this study for detecting polygenic selection can be accessed from zenodo repository(https://zenodo.org/records/12752105)