Oghenejokpeme I. Orhobor

This paper evaluates machine learning methods for phenotype prediction in yeast, rice, and wheat, finding that standard ML methods often outperform classical statistical genetics approaches.

This study uses marker stability to predict agronomic traits and perform association mapping in rice landraces, showing improved performance over GEMMA for simulated traits and identifying markers in known QTL regions.