  PURPOSE: We propose a fully unsupervised method to learn latent disease
networks directly from unstructured biomedical text corpora. This method
addresses current challenges in unsupervised knowledge extraction, such as the
detection of long-range dependencies and requirements for large training
corpora. METHODS: Let C be a corpus of n text chunks. Let V be a set of p
disease terms occurring in the corpus. Let X indicate the occurrence of V in C.
Gextext identifies disease similarities by positively correlated occurrence
patterns. This information is combined to generate a graph on which geodesic
distance describes dissimilarity. Diseasomes were learned by Gextext and GloVE
on corpora of 100-1000 PubMed abstracts. Similarity matrix estimates were
validated against biomedical semantic similarity metrics and gene profile
similarity. RESULTS: Geodesic distance on Gextext-inferred diseasomes
correlated inversely with external measures of semantic similarity. Gene
profile similarity also correlated significant with proximity on the inferred
graph. Gextext outperformed GloVE in our experiments. The information contained
on the Gextext graph exceeded the explicit information content within the text.
CONCLUSIONS: Gextext extracts latent relationships from unstructured text,
enabling fully unsupervised modelling of diseasome graphs from PubMed
abstracts.
