A visual analytics tool to enhance pathway models with literature-based evidence and confidence: LitPathExplorer

CHRYSOULA ZERVA; SOPHIA ANANIADOU; AXEL J. SOTO

Basilea

Workshop; Intelligent Systems for Molecular Biology (ISMB) European Conference on Computational Biology (ECCB); 2019

ICMB/ECCB

Biomedical interaction networks and pathway models are invaluable resources for understanding and experimenting with the mechanisms underpinning complex biological processes. So far, their curation, maintenance and update is carried out mostly manually, involving thorough inspection of related scientific literature and frequent updates. Navigating through and discovering interactions, among the rapidly increasing amounts of scientific literature, is a complex and time-consuming process, which could be aided by computational methods. Text mining and citation analysis have proven to be valuable towards this goal, enabling automated identification of biomolecular interactions in text and linking them to related pathways. Interpretable integration of such methods into pathway tools could expedite pathway curation and related research.We address the aforementioned challenges, proposing LitPathExplorer, which combines advanced text mining methods and interactive visualisation functionalities. LitPathExplorer, mines large document collections to identify corroborating evidence for existing pathway interactions and propose new interactions. Contextual and bibliometric information is used to complement each interaction with confidence metrics. We present use-cases from two cancer research areas, where LitPathExplorer was well received by researchers. We also discuss performance in terms of precision and demonstrate how citation analysis can further improve precision in identifying the relevance of new text-mined interactions to a pathway.