Integration of Gene Regulatory Pathways Found in the Literature in a Graph Database

Gene regulatory pathways plays a significant role in personalized medicine. Because pathways often branch or converge, graphs are better at describing them. In this work, we introduced Neo4j, a graph database, to represent gene pathways collected from the KEGG Pathway database and from 217 non-small cell lung cancer (NSCLC) articles retrieved from PubMed. We found that the graph representation of disease pathways was able to display regulatory relations between two genes even though they were not mentioned in the same article. Besides, contradictory pathway relations, for example “EGFR activates RAS” and “EGFR inhibits RAS”, self-activation or self-inhibition like “AKT activates AKT”, and pathways with different direction like “EGFR activates RAS” and “RAS activates EGFR” may also be investigated. This contradictory information may come from different conditions of the relations, different scientific opinions or they be mistakes made by our deep learning pipeline that extracts information from the literature. Overall, we concluded that graph representations of disease pathways can give us a panoramic view of the pathway knowledge obtained from various kinds of sources. Although the representation may not manifest real underlying mechanisms, it helps provide research focus and propel the development of personalized medicine.