Abstract: Universal signatures represent generalizable features that are informative for generating predictions for different disease activities across different diseases. More specifically, one or more universal signatures are learned from data pertaining to a first disease indication and then applied to generate predictions for a one or more additional disease indications. The implementation of one or more universal signatures is useful for generating predictions for disease indications, such as disease indications involving rare or novel diseases, where it may be infeasible to develop a model due to insufficient training data.

Abstract: Described are genomic health risk metrics elaborated herein to hold significant advantages for the health care industry. The likelihood that any given GSV will be deleterious is relatively small. Since every human genome sequenced may result in several million GSVs, the advantage of a genomic health risk metric such as a tolerability score, an n-mer score, a context dependent tolerance score, or a protein tolerability score to clinicians is that it will allow them to focus on and prioritize deleterious mutations.