On crowd-verification of biological networks

Abstract

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-verifcation 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. Gamifcation principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identifed and verifed. The resulting network models will represent the current status of biological knowledge within the defned 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 scientifc community. © The author(s), publisher and licensee Libertas Academica Ltd.