Finding Text-Supported Gene-to-Disease Co-appearances with MOPED-Digger

Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, MOPED-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them. Analysis of 18,159,237 PubMed title/abstracts yielded 1,796,799 gene/disease co-appearances that can be used to focus attention on the most promising publications for a possible gene/disease association. An integrated score is provided to enable assessment of broadly presented published evidence to capture more tenuous connections. MOPED-Digger is written in Java and uses Apache Lucene 5.0 library. The utility runs as a command-line program with a variety of user-options and is freely available for download from the MOPED 3.0 website (moped.proteinspire.org).