The potential of deep learning (DL) in genomic selection (GS) is constrained by the significant technical expertise required to design and implement neural networks. While DL has revolutionized fields like language processing and structural biology, its application in GS has not yet consistently outperformed traditional models like mixed linear models. The key to unlocking DL's power in GS lies in the exploration of network architectures tailored to genomic data, a process that demands intensive programming and poses a barrier for many researchers. To overcome this challenge, we developed AI4EVER, a software tool with an intuitive graphical user interface (GUI) that enables users to explore and apply machine learning models without any coding. Built on a Swift/SwiftUI frontend and a Python backend, AI4EVER supports optional integration of GWAS p-values as feature weights during model training, while offering customizable parameters, real-time visualization of prediction accuracy (e.g., correlation, RMSE), and automated generation of feature importance for model interpretation. The platform currently supports five models: Ridge Regression, Random Forest, Gradient Boosted Decision Trees, Multi-Layer Perceptron, and a customizable Keras-based neural network. AI4EVER also separates model training and prediction workflows, allowing trained models to be reused for independent prediction datasets. By democratizing access to advanced AI, AI4EVER empowers genomic researchers to accelerate data-driven decision-making in breeding programs, ultimately lowering the barrier to artificial intelligence-enabled crop and animal improvement.

To perform the Neural Network Computation, please run on a MacOS 11 or above system, download Python 3.8.5 and the "installpkg(Mac)" shown below for the necessary Python libraries.