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Cell Syst. 2019 Apr 24;8(4):275-280. doi: 10.1016/j.cels.2019.03.013.

Strategies for Network GWAS Evaluated Using Classroom Crowd Science.

Author information

1
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
2
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
3
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.
4
Program in Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA.
5
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: tideker@ucsd.edu.

Abstract

Biological networks can substantially boost power to identify disease genes in genome-wide association studies. To explore different network GWAS methods, we challenged students of a UC San Diego graduate level bioinformatics course, Network Biology and Biomedicine, to explore and improve such algorithms during a four-week-long classroom competition. Here, we report the many creative solutions and share our experiences in conducting classroom crowd science as both a research and pedagogical tool.

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