Unsupervised co-optimization of a graph neural network and a knowledge graph embedding model to prioritize causal genes for Alzheimer’s Disease

1. Data obtained from clinical trials for a given disease often capture reliable empirical features of the highest quality which are limited to few studies/experiments. In contrast, knowledge data extracted from biomedical literature captures a wide range of clinical information relevant to a given disease that may not be as reliable as the experimental data. Therefore, we propose a novel method of training that co-optimizes two AI algorithms on experimental data and knowledge-based information from literature respectively to supplement the learning of one algorithm with that of the other and apply this method to prioritize/rank causal genes for Alzheimer’s Disease (AD). One algorithm generates unsupervised embeddings for gene nodes in a protein-protein interaction network associated with experimental data. The other algorithm generates embeddings for the nodes/entities in a knowledge graph constructed from biomedical literature. Both these algorithms are co-optimized to leverage information from each other’s domain. Therefore; a downstream inferencing task to rank causal genes for AD ensures the consideration of experimental and literature data available to implicate any given gene in the geneset. Rank-based evaluation metrics computed to validate the gene rankings prioritized by our algorithm showed that the top ranked positions were highly enriched with genes from a ground truth set that were experimentally verified to be causal for the progression of AD.