An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

Abstract In phenotype prediction the physical character of an organism is predicted from knowledge of its genotype and environment. Such studies are of the highest societal importance as they are now of central importance to medicine, crop-breeding, etc. We investigated two phenotype prediction problems: one simple and clean (yeast), the other complex and real-world (wheat). We compared standard machine learning methods (forward stepwise regression, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM)) with two state-of-the-art classical statistical genetics methods (including genomic BLUP). Additionally, using the yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, population structure, genotype drift, and the use of different data…


Link to Full Article: An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!