학술논문
GenoML: Automated Machine Learning for Genomics
Document Type
Working Paper
Author
Makarious, Mary B.; Leonard, Hampton L.; Vitale, Dan; Iwaki, Hirotaka; Saffo, David; Sargent, Lana; Dadu, Anant; Castaño, Eduardo Salmerón; Carter, John F.; Maleknia, Melina; Botia, Juan A.; Blauwendraat, Cornelis; Campbell, Roy H.; Hashemi, Sayed Hadi; Singleton, Andrew B.; Nalls, Mike A.; Faghri, Faraz
Source
Subject
Language
Abstract
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.