Understanding gene-disease association via machine learning
Kanikah Mehndiratta, MSc, University of Glasgow Understanding the association between diseases and the potential genes involved can be challenging. Usually, scientists prefer biomedical experiments to explore the underlying genetics of a complex disease. But the experiments turn out to be very expensive and time consuming. They also pose risk for human error which can lead […]