RatsPub: a webservice aided by deep learning to mine PubMed for addiction-related genes

Interpreting and integrating results from omics studies typically requires a comprehensive and time consuming survey of extant literature. Here, we introduce GeneCup, an easy to use literature mining web service that searches all PubMed abstracts for user-provided gene symbols in conjunction with a set of custom keywords organized into a customized ontology, as well as results from human genome-wide association studies (GWAS). As an example, we organized over 300 keywords related to drug addiction into seven categories. The literature search is conducted by querying the NIH PubMed server using a programming interface, which is followed by retrieving abstracts from a local copy of the PubMed archive. The main results presented to the user are individual sentences containing the gene symbol, organized by the keywords they also contain. These sentences are presented through an interactive graphical interface or as tables. GWAS results are displayed using a similar method. All results are linked to the original abstract in PubMed. In addition, a convolutional neural network is employed to distinguish sentences describing systemic stress from those describing cellular stress. The automated and comprehensive search strategy provided by GeneCup facilitates the integration of new discoveries from omic studies with existing literature. GeneCup is free and open source software. The source code of GeneCup and the link to a running instance is available at https://github.com/hakangunturkun/GeneCup