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easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization
SUMMARY: Predicting complex traits from genotypic information is a major challenge in various biological domains. With easyPheno, we present a comprehensive Python framework enabling the rigorous training, comparison and analysis of phenotype predictions for a variety of different models, ranging fr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101695/ https://www.ncbi.nlm.nih.gov/pubmed/37066135 http://dx.doi.org/10.1093/bioadv/vbad035 |
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author | Haselbeck, Florian John, Maura Grimm, Dominik G |
author_facet | Haselbeck, Florian John, Maura Grimm, Dominik G |
author_sort | Haselbeck, Florian |
collection | PubMed |
description | SUMMARY: Predicting complex traits from genotypic information is a major challenge in various biological domains. With easyPheno, we present a comprehensive Python framework enabling the rigorous training, comparison and analysis of phenotype predictions for a variety of different models, ranging from common genomic selection approaches over classical machine learning and modern deep learning-based techniques. Our framework is easy-to-use, also for non-programming-experts, and includes an automatic hyperparameter search using state-of-the-art Bayesian optimization. Moreover, easyPheno provides various benefits for bioinformaticians developing new prediction models. easyPheno enables to quickly integrate novel models and functionalities in a reliable framework and to benchmark against various integrated prediction models in a comparable setup. In addition, the framework allows the assessment of newly developed prediction models under pre-defined settings using simulated data. We provide a detailed documentation with various hands-on tutorials and videos explaining the usage of easyPheno to novice users. AVAILABILITY AND IMPLEMENTATION: easyPheno is publicly available at https://github.com/grimmlab/easyPheno and can be easily installed as Python package via https://pypi.org/project/easypheno/ or using Docker. A comprehensive documentation including various tutorials complemented with videos can be found at https://easypheno.readthedocs.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. |
format | Online Article Text |
id | pubmed-10101695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101016952023-04-14 easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization Haselbeck, Florian John, Maura Grimm, Dominik G Bioinform Adv Application Note SUMMARY: Predicting complex traits from genotypic information is a major challenge in various biological domains. With easyPheno, we present a comprehensive Python framework enabling the rigorous training, comparison and analysis of phenotype predictions for a variety of different models, ranging from common genomic selection approaches over classical machine learning and modern deep learning-based techniques. Our framework is easy-to-use, also for non-programming-experts, and includes an automatic hyperparameter search using state-of-the-art Bayesian optimization. Moreover, easyPheno provides various benefits for bioinformaticians developing new prediction models. easyPheno enables to quickly integrate novel models and functionalities in a reliable framework and to benchmark against various integrated prediction models in a comparable setup. In addition, the framework allows the assessment of newly developed prediction models under pre-defined settings using simulated data. We provide a detailed documentation with various hands-on tutorials and videos explaining the usage of easyPheno to novice users. AVAILABILITY AND IMPLEMENTATION: easyPheno is publicly available at https://github.com/grimmlab/easyPheno and can be easily installed as Python package via https://pypi.org/project/easypheno/ or using Docker. A comprehensive documentation including various tutorials complemented with videos can be found at https://easypheno.readthedocs.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2023-03-22 /pmc/articles/PMC10101695/ /pubmed/37066135 http://dx.doi.org/10.1093/bioadv/vbad035 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Note Haselbeck, Florian John, Maura Grimm, Dominik G easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title |
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title_full |
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title_fullStr |
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title_full_unstemmed |
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title_short |
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization |
title_sort | easypheno: an easy-to-use and easy-to-extend python framework for phenotype prediction using bayesian optimization |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101695/ https://www.ncbi.nlm.nih.gov/pubmed/37066135 http://dx.doi.org/10.1093/bioadv/vbad035 |
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