GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain expertise to clean, pre-process, harmonize and perform quality control of the data. Furthermore, tuning, validation, and interpretation involve taking into account the biology and possibly the limitations of the underlying data collection, protocols, and technology. GenoML's mission is to bring machine learning for genomics and clinical data to non-experts by developing an easy-to-use tool that automates the full development, evaluation, and deployment process. Emphasis is put on open science to make workflows easily accessible, replicable, and transferable within the scientific community. Source code and documentation is available at https://genoml.com.
@article{arxiv.2103.03221,
title = {GenoML: Automated Machine Learning for Genomics},
author = {Mary B. Makarious and Hampton L. Leonard and Dan Vitale and Hirotaka Iwaki and David Saffo and Lana Sargent and Anant Dadu and Eduardo Salmerón Castaño and John F. Carter and Melina Maleknia and Juan A. Botia and Cornelis Blauwendraat and Roy H. Campbell and Sayed Hadi Hashemi and Andrew B. Singleton and Mike A. Nalls and Faraz Faghri},
journal= {arXiv preprint arXiv:2103.03221},
year = {2021}
}