Back to Search Start Over

GenoML: Automated Machine Learning for Genomics

Authors :
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
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
Publication Year :
2021

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.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1269533569
Document Type :
Electronic Resource