On Crowd-verification of Biological Networks

• Main Authors: Sam Ansari, Jean Binder, Stephanie Boue, Anselmo Di Fabio, William Hayes, Julia Hoeng, Anita Iskandar, Robin Kleiman, Raquel Norel, Bruce O'neel, Manuel C. Peitsch, Carine Poussin, Dexter Pratt, Kahn Rhrissorrakrai, Walter K. Schlage, Gustavo Stolovitzky, Marja Talikka
• Format: Article
• Language: English
• Published: SAGE Publishing 2013-01-01
• Series: Bioinformatics and Biology Insights
• Online Access: https://doi.org/10.4137/BBI.S12932

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.