Hi, this is Steven Cherry for IEEE Spectrum’s podcast, Fixing the Future.
Rare diseases are, well, rare. In two not unrelated ways. By definition, they’re diseases that afflict fewer than 200,000 people. But because, in the world of big business, in particular big pharma, that’s not enough to bother with, that is, it’s not profitable enough to bother with, rare diseases are rarely worked on, to say nothing of cured.
For example, hypertryptophanemia is a rare condition that likely occurs due to abnormalities in the body’s ability to process the amino acid, tryptophan. How rare? I don’t know. A Google search didn’t yield an answer to that question. In fact, it’s rare enough that Google didn’t autocomplete the word even with 15 of its 19 letters typed in.
Paradoxically, big data has the potential to change that. Because 200,000 is, after all, a lot of data points. But it presents problems of its own. There isn’t one giant pool of 200,000 data points. So the first challenge is to aggregate all the potential data that’s out there. And the big challenge there is that a lot of the data is contained, not in beautifully homogeneous, joinable, relatable databases. It’s buried deep in documents like PubMed articles and patent filings.
Deep Learning can help researchers pull that data out of those documents. At least, that’s the strategy of a startup called Vyasa. Here to explain it is Vyasa’s CEO and founder, Christopher Bouton.
Chris, welcome to the podcast.