PT  - JOURNAL ARTICLE
AU  - Mustafa Hakan Gunturkun
AU  - Efraim Flashner
AU  - Tengfei Wang
AU  - Megan K. Mulligan
AU  - Robert W. Williams
AU  - Pjotr Prins
AU  - Hao Chen
TI  - RatsPub: a webservice aided by deep learning to mine PubMed for addiction-related genes
AID  - 10.1101/2020.09.17.297358
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.09.17.297358
4099  - http://biorxiv.org/content/early/2021/01/13/2020.09.17.297358.short
4100  - http://biorxiv.org/content/early/2021/01/13/2020.09.17.297358.full
AB  - Interpreting and integrating results from omics studies on addiction phenotypes require a comprehensive survey of the extant literature. Most often this is conducted by ad hoc queries of the PubMed database. Here, we introduce RatsPub, a literature mining web service that searches user-provided gene symbols in conjunction with a set of systematically curated keywords related to addiction, as well as results from human genome-wide association studies (GWAS). We have organized over 300 keywords into seven categories forming an ontology. The literature search is conducted by querying the NIH PubMed server using a programmatic interface. Abstracts are retrieved from a local copy of the PubMed archive. The main results presented to the user are sentences containing the gene symbol and at least one keyword. These sentences are presented in the browser through an interactive graphical interface or using tables. Results are linked to the source PubMed records. GWAS results are displayed using a similar method. We wrote a natural language processing module that uses deep convolutional neural networks to distinguish sentences describing systemic stress vs cellular stress. The automated and comprehensive search strategy provided by RatsPub facilitates the integration of new discoveries from addiction omic studies with existing literature and improves analysis and modeling in the field of addiction biology. RatsPub is free and open source software. The source code of RatsPub and the link to a running instance is available at https://github.com/chen42/ratspub.Competing Interest StatementThe authors have declared no competing interest.