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 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 ImplementationeasyPheno 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.
Supplementary informationA comprehensive documentation including various tutorials complemented with videos can be found at https://easypheno.readthedocs.io/. In addition, we provide examples of how to use easyPheno with real and simulated data in the Supplementary.
- Publikationsart
- Zeitschriftenbeiträge (peer-reviewed)
- Titel
- easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization
- Medien
- Bioinformatics Advances
- Band
- 2023
- ISBN
- 2635-0041
- Autoren
- Florian Haselbeck , Maura John , Prof. Dr. Dominik Grimm
- Herausgeber
- Oxford University Press
- Veröffentlichungsdatum
- 22.03.2023
- Zitation
- Haselbeck, F.; John, M.; Grimm, D. (2023): easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization. Bioinformatics Advances 2023. DOI: 10.1093/bioadv/vbad035