Machine Learning Models for Phenotype Prediction from Genotype

Phenotype prediction refers to estimation or prediction of an organism's observable characteristics, or phenotypes, based on its genetic information or other relevant factors. The problem is important for several reasons. Phenotype prediction can contribute to a personalized medicine, prediction of disease risks, prognosis, and treatment response. In this paper, we develop quantitative phenotype regression prediction models to predict the values of phenotypes from various genotype features based on several machine learning and deep learning models, such as elastic net, a multilayer perceptron (MLP), convolutional neural network (CNN) and multi-task learning. The comparison results on yeast data show that our proposed elastic net and CNN model outperforms the existing models for 20 phenotype predictions.