  In this work, we consider the exponentially growing subarea of genetics in
cancer. The need to synthesize and centralize this evidence for dissemination
has motivated a team of physicians to manually construct and maintain a
knowledge base that distills key results reported in the literature. This is a
laborious process that entails reading through full-text articles to understand
the study design, assess study quality, and extract the reported cancer risk
estimates associated with particular hereditary cancer genes (i.e.,
penetrance). In this work, we propose models to automatically surface key
elements from full-text cancer genetics articles, with the ultimate aim of
expediting the manual workflow currently in place.
  We propose two challenging tasks that are critical for characterizing the
findings reported cancer genetics studies: (i) Extracting snippets of text that
describe \emph{ascertainment mechanisms}, which in turn inform whether the
population studied may introduce bias owing to deviations from the target
population; (ii) Extracting reported risk estimates (e.g., odds or hazard
ratios) associated with specific germline mutations. The latter task may be
viewed as a joint entity tagging and relation extraction problem. To train
models for these tasks, we induce distant supervision over tokens and snippets
in full-text articles using the manually constructed knowledge base. We propose
and evaluate several model variants, including a transformer-based joint entity
and relation extraction model to extract <germline mutation, risk-estimate>}
pairs. We observe strong empirical performance, highlighting the practical
potential for such models to aid KB construction in this space. We ablate
components of our model, observing, e.g., that a joint model for <germline
mutation, risk-estimate> fares substantially better than a pipelined approach.
