If we aggregated all the data from countless years of research, might we learn something new about ourselves, the diseases that infect us, and possible treatments?
That’s the hope behind the Biomedical Data Translator program, launched by the NIH in 2016: to create a “Google” for biomedical data that could sift through hundreds of separate data sources to help researchers connect “dots” in datasets with distinct formats and peculiarities.
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The program has awarded about $17.5 million to 19 institutions across the country that are working to integrate years of data, ranging from electronic health records to genomic sequences, that had previously been spread across a variety of platforms, and then applying new machine learning tools to help organize and reason through the wealth of information.
This means that, unlike Google, the Translator would be able to make connections between datasets that had not previously been associated with each other.
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The goals of this program are ambitious, including to show “every disease that has symptom X and/or affects a particular cell type,” but those involved remain steadfast in their hope to make lasting change in the way patients are treated.
Read full, orignal post: NIH-funded project aims to build a ‘Google’ for biomedical data
