Disease Clustering with Process Annotations from Gene Ontology

This paper presents a disease clustering approach by utilizing the biological process annotations from the Gene Ontology as the only data source for clustering diseases. As a result, a disease within a cluster should be more similar to all other diseases in the same cluster than to any disease in other clusters. Essentially, the clustering task is an unsupervised machine learning technique that attempts to discover and learn some hidden patterns from the disease information to place similar diseases together in the same cluster. We used two independent validations to examine our results. We examined the path length between disease pairs in the same cluster versus pairs in two separate clusters by utilizing semantic relationships from the Disease Ontology. We also utilized recently published results on disease similarity from a comprehensive study. Our experimental results are highly encouraging and highly agree with both validation methods. Specifically, most diseases placed in one cluster by our method are more similar to one another than to any disease in the other cluster, according to the validation results.