Sparse data. Trying to match the growing body of genomic datasets with associated clinical data is difficult for a variety of reasons. Most importantly, while genomic data are standardized and can be generated at scale, clinical data are often unstructured and sparse, making it difficult to represent a phenotype fully through any type of abbreviated format. Quite frequently in our prior blog posts, we have discussed the Human Phenotype Ontology (HPO), a standardized dictionary where all phenotypic features can be mapped and linked. But these data also quickly become large and the question on how best to handle them remains. In a recent publication, we translated more than 53M patient notes using HPO and explored the utility of vector embedding, a method that currently forms the basis of many AI-based applications. Here is a brief summary on how these technologies can help us to better understand phenotypes. Continue reading →