Graph Machine Learning in Genomic Prediction - KDnuggets

Deep learning is widely known for its flexibility and the capability to uncover complex patterns in large datasets; with these advantages, instances of deep learning in the genomics domain are emerging. One such application is genomic prediction, where the traits of individuals -- like susceptibility to disease or yield-related traits -- are predicted using their genomic information. Understanding the correlation of the genetic traits and variations in genomes could have many benefits such as advancing crop breeding processes, and hence improve food security. In this article, we explore how genetic relationships can be exploited alongside genomic information to predict genetic traits, with the aid of graph machine learning algorithms. In genomic prediction, traditional deep learning would use an individual's genomic information -- like a single nucleotide polymorphism (SNP) -- as input features to the neural network. A SNP is essentially a difference that occurs at a specific position in an individual's genome.