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Expert Opin Drug Discov. 2017 Aug;12(8):849-857. doi: 10.1080/17460441.2017.1335302. Epub 2017 Jun 6.

Novel approaches to develop community-built biological network models for potential drug discovery.

Author information

a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland.
b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA.
c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA.
d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany.
e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.
f Protavio Ltd , Stevenage , UK.


Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.


Biological Expression Language; Mechanistic; crowd sourcing; network model; ontology; pathway

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