NEW YORK (GenomeWeb) – Harvard Medical School startup Nebula Genomics said today that it has entered into a research partnership with Hong Kong-based Longenesis in a field the two companies call "life data economics."
The partners said they would apply artificial intelligence and blockchain technology to create a platform for individuals and biobanks to store, manage, and control access to genomic and other health data, including laboratory results. Researchers would be able to buy access to this data in a secure environment.
“By allowing individuals and large data providers such as biobanks to maintain ownership of their genomic data on our platform and profit from it, Nebula Genomics seeks to incentivize generation of genomic data," Nebula cofounder George Church, a professor at Harvard Medical School, said in a statement. "In doing so, we will gather the data on a single network where it can be conveniently and securely accessed by researchers. In other words, we will make a marketplace that will create an equitable and efficient economy for genomic data."
Longenesis, which itself is a partnership between Insilico Medicine, an AI company, and the Bitfury Group, a blockchain technology company, offers a similar online environment that instead of genomics focuses on longitudinal health data. "Our platforms complement one another very well," Church said.
The new partners said they would break down some of the silos that have long hindered this kind of data movement, brokering, and usage.
"Currently, acquiring genomic data is a slow and costly process, because researchers at pharma and biotech companies have to manually inquire about data availability, negotiate prices, sign contracts, and make payments. We will use smart contracts to automate data acquisition and make it several orders of magnitude faster," Nebula Genomics Chief Security Officer and Cofounder Dennis Grishin said in a statement.
Longenesis and Nebula claim that their work will establish the fields of "microdataeconomics" — which they called the "study of the value of life data that is used for drug discovery, such as proteomics or data regarding the structure and activity of specific molecules, both in vitro and in vivo" — and "macrodataeconomics." They described the latter as the "study of the value of life data that is used to determine human health such as electronic health records and genomics."