Method and apparatus for classification and/or prioritization of genetic variants

A method executed in on or more processing resources, the method comprising:

• receiving input data comprising an indication of a biological relationship of a genetic variant of the patient with other genetic variants in a reference data set of known disease causing genetic variants at a first plurality of trained nodes of a hierarchical Bayesian Network;
• using the first plurality of trained nodes to determine a likelihood of the biological relationship of the genetic variant with other genetic variants in the reference data set of known disease causing genetic variants occurring;
• receive input data comprising a plurality of clinical features of the patient in relation to other pluralities of clinical features in a reference data set of known clinical features at a second plurality of trained nodes of a hierarchical Bayesian Network;
• using the second plurality of trained nodes to determine a likelihood of the data comprising the plurality of clinical features of the patient occurring in relation to the reference data set of known clinical features;
• at least one of:
• attributing a disease label to the received input data;
• or
• determining an indication of a likelihood of co-occurrence of the genetic variant of the patient and the plurality of clinical features of the patient;
• using one or more further nodes based on input from the first plurality of trained nodes and from the second plurality of trained nodes.

A method of training a hierarchical Bayesian Network for classifying genetic variants comprising:

• identifying one or more nodes that provide a low predictive quality within the hierarchical Bayesian Network;
• using biological experiments to generate training data relating to the identified one or more nodes or relating to an observed node providing input to the identified one or more nodes;
• and
• training said hierarchical Bayesian Network using said training data.