Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts.

We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study.

Large-scale automated machine reading discovers new cancer-driving mechanisms.

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated 'big mechanisms' with extracted 'big data' can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.