From R24 "We will develop algorithms that will weight feature frequencies and negative annotations using our Bayesian ontology-querying framework89, which we will extend from a purely HPO-based algorithm to a cross-species inference algorithm accounting for species differences. Our probabilistic framework also provides a natural framework for integrating quantitative data; existing phenotype ontologies provide ready-made ‘bins’ for quantitative phenotypes, such as ‘increased body weight’ or ‘abnormal femur size’. Terms such as these are used both in the model organism literature, and by the curators of model organism databases. Sometimes this is a judgment call on the part of a researcher, but in the case of many phenotyping pipelines, normal and abnormal ranges of measurements are defined as part of the protocol. We will use these protocols and the data gather from phenotyping pipelines to learn and interpolate normal ranges for a variety of phenotypes. This will allow us to combine both qualitatively assigned abnormal ranges with quantitative data. We will also improve our capacity for differential diagnostics as well as for novel disease gene discovery by including these new advanced scoring metrics within our website as well as in our tools such as Exomiser."
jmcmurry
commented
7 years ago
From R24 "We will develop algorithms that will weight feature frequencies and negative annotations using our Bayesian ontology-querying framework89, which we will extend from a purely HPO-based algorithm to a cross-species inference algorithm accounting for species differences. Our probabilistic framework also provides a natural framework for integrating quantitative data; existing phenotype ontologies provide ready-made ‘bins’ for quantitative phenotypes, such as ‘increased body weight’ or ‘abnormal femur size’. Terms such as these are used both in the model organism literature, and by the curators of model organism databases. Sometimes this is a judgment call on the part of a researcher, but in the case of many phenotyping pipelines, normal and abnormal ranges of measurements are defined as part of the protocol. We will use these protocols and the data gather from phenotyping pipelines to learn and interpolate normal ranges for a variety of phenotypes. This will allow us to combine both qualitatively assigned abnormal ranges with quantitative data. We will also improve our capacity for differential diagnostics as well as for novel disease gene discovery by including these new advanced scoring metrics within our website as well as in our tools such as Exomiser."