Automated literature mining and hypothesis generation through a network of Medical Subject Headings
The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations among genes, drugs and diseases from PubMed and predicts new ones.\n\nComparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We also show that many predictions based on the literature prior to 2014 were published subsequently. In a practical application, we validated experimentally a surprising new association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the literature (http://meteor.lichtargelab.org/).\n\nAUTHOR SUMMARYThe large size and exponential expansion of the scientific literature forms a bottleneck to accessing and understanding published findings. Manual curation and Natural Language Processing (NLP) aim to address this bottleneck by summarizing and disseminating the knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the publications and demonstrated a comparable coverage but higher accuracy than current NLP methods. Furthermore, we combined the extracted knowledge with semi-supervised machine learning to create hypotheses to guide future work and discovered a direct interaction between two important cancer genes.