  As genetic sequencing costs decrease, the lack of clinical interpretation of
variants has become the bottleneck in using genetics data. A major rate
limiting step in clinical interpretation is the manual curation of evidence in
the genetic literature by highly trained biocurators. What makes curation
particularly time-consuming is that the curator needs to identify papers that
study variant pathogenicity using different types of approaches and
evidences---e.g. biochemical assays or case control analysis. In collaboration
with the Clinical Genomic Resource (ClinGen)---the flagship NIH program for
clinical curation---we propose the first machine learning system, LitGen, that
can retrieve papers for a particular variant and filter them by specific
evidence types used by curators to assess for pathogenicity. LitGen uses
semi-supervised deep learning to predict the type of evidence provided by each
paper. It is trained on papers annotated by ClinGen curators and systematically
evaluated on new test data collected by ClinGen. LitGen further leverages rich
human explanations and unlabeled data to gain 7.9%-12.6% relative performance
improvement over models learned only on the annotated papers. It is a useful
framework to improve clinical variant curation.
