In a unique project, researchers at the Icahn School of Medicine at Mount Sinai have crowdsourced the annotation and analysis of a large number of gene expression profiles from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO). More than 70 volunteers from 25 countries helped Mount Sinai researchers analyze the data, enabling the identification of new associations between genes, diseases, and drugs – something that a smaller number of unaided researchers, or an automated computer program, would not be able to achieve. An article published today in the journal Nature Communications describes the crowdsourcing project.

Omics repositories, which are virtual storehouses for raw gene expression data, contain thousands of studies. Such an abundance of data opens opportunities for integrative analyses that can uncover new knowledge that was missed or was not possible in the initial publication of the data. For example, while a dataset from a given study may have been used for a particular published article, that same dataset may contain evidence whose value can only become realized when combined with data from another study. Then, it might become apparent that a drug can be repurposed to treat a different disease. Several computerized search engines have been designed to comb through this data. However, for these tools to be effective they require heavy, time-consuming human curation to ensure accuracy.

Source: Crowdsourcing for scientific discovery: Researchers find novel ways to analyze data for drug and target discovery