Prediction Models

easyPheno includes various phenotype prediction models, both classical genomic selection approaches as well as machine and deep learning-based methods. In the following pages, we will give some details for all of the currently implemented models. We further included a subpage explaining the Bayesian optimization that we use for our automatic hyperparameter search.

We provide both a workflow running easyPheno with a command line interface using Docker and as a pip package, see the following tutorials for more details:

• HowTo: Run easyPheno using Docker

• HowTo: Use easyPheno as a pip package

In both cases, you need to select the prediction model you want to run - or also multiple ones within the same optimization run. A specific prediction model can be selected by giving the name of the .py file in which it is implemented (without the .py suffix). For instance, if you want to run a Support Vector Machine implemented in svm.py, you need to specify svm.

easyPheno automatically chooses based on the selected phenotype whether to use the implementation for a classification (discrete trait) or regression (continuous trait) task. All models except the classical genomic selection approaches (RR-BLUP and Bayesian alphabet models) provide an implementation for both cases.

In the following table, we give the keys for all prediction models as well as links to detailed descriptions and the source code:

Phenotype Prediction Models

Model	Key in easyPheno	Description	Source Code	Notes
Ridge Regression BLUP	blup	RR-BLUP	blup.py
Bayes A	bayesAfromR	Bayesian alphabet	bayesAfromR.py	requires Docker workflow
Bayes B	bayesBfromR	Bayesian alphabet	bayesBfromR.py	requires Docker workflow
Bayes C	bayesCfromR	Bayesian alphabet	bayesCfromR.py	requires Docker workflow
L1-regularized Linear / Logistic Regression	linearregression	Linear and Logistic Regression	linearregression.py	Regularization type in source code adjustable
Elastic Net-regularized Linear / Logistic Regression	elasticnet	Linear and Logistic Regression	elasticnet.py
Support Vector Machine / Regression	svm	Support Vector Machine / Regression	svm.py
Random Forest	randomforest	Random Forest	randomforest.py
XGBoost	xgboost	XGBoost	xgboost.py
Multilayer Perceptron	mlp	Multilayer Perceptron	mlp.py
Convolutional Neural Network	cnn	Convolutional Neural Network	cnn.py
Local Convolutional Neural Network	localcnn	Local Convolutional Neural Network	localcnn.py

If you are interested in adjusting an existing model or its hyperparameters: HowTo: Adjust existing prediction models and their hyperparameters.

If you want to integrate your own prediction model: HowTo: Integrate your own prediction model.