  Hypothesis generation is becoming a crucial time-saving technique which
allows biomedical researchers to quickly discover implicit connections between
important concepts. Typically, these systems operate on domain-specific
fractions of public medical data. MOLIERE, in contrast, utilizes information
from over 24.5 million documents. At the heart of our approach lies a
multi-modal and multi-relational network of biomedical objects extracted from
several heterogeneous datasets from the National Center for Biotechnology
Information (NCBI). These objects include but are not limited to scientific
papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses
using Latent Dirichlet Allocation applied on abstracts found near shortest
paths discovered within this network, and demonstrate the effectiveness of
MOLIERE by performing hypothesis generation on historical data. Our network,
implementation, and resulting data are all publicly available for the broad
scientific community.
