UPDATED: Stanford spinoff snags $10M first round, mines Big Data in biotech
Investors have banked on the startup Ayasdi to solve Big Data problems in life sciences, national security and other industries. The Stanford University spinoff has raised $10.25 million in a Series A round from Khosla Ventures and Floodgate, with the startup revealing several projects with top biotech researchers.
As discussed last week at FierceBiotech's "Big Data Biopharma Forum" in San Francisco, one of the major hurdles in extracting value from big, hairy data sets is asking the right questions of the data. Ayasdi points out that query-driven approaches are flawed because of inherent human biases. The company offers a machine-learning approach that automatically asks questions for huge data sets to advance discoveries.
Ayasdi, founded in 2008, isn't the first company to tackle Big Data challenges with machine learning. Cambridge, MA-based GNS Healthcare has worked in this arena for more than a decade. Yet Ayasdi claims to be the first to combine computer science with a new field of mathematics called "topological data analysis" that visualizes massive data sets at once. Stanford math professor Gunnar Carlsson, an expert in this field, has won support for his research from DARPA and the National Science Foundation.
"Ayasdi has already helped us glean new insights that will lead to breakthrough drug discoveries," industry heavyweight Eric Schadt, director of the Icahn Institute for Genomics and Multiscale Biology at Mount Sinai, said in a statement.
In addition to Mount Sinai, the University of California-San Francisco has tapped Ayasdi's technology in a collaboration to advance diagnostics and therapies for brain and spinal trauma. Ayasdi also touted its work in drug discovery, with its extremely fast approach speeding up findings. It's also discovered patterns from a massive data set on breast cancer.
"The ability of generating data has really increased [but] analysis has not really changed in the past 20 years," Gurjeet Singh, CEO of Ayasdi, told FierceBiotechIT in an interview. "If you go into the biopharma industry, people are using classical statistical and machine-learning methods."
Merck ($MRK) and two other large drugmakers have at least test-driven the software platform from Ayasdi, Singh said. Those companies have applied the machine learning platform to data related drug discovery and development, but the CEO was unable to reveal specifics of his pharma customers' use of the software.
It can take a decade or more from the time scientists begin chasing a big idea in biology to when drugmakers can march into a proof-of-concept drug study in humans. And most programs crash and burn during the development process after pharma outfits spend hundreds of millions of dollars or more on the efforts. Ayasdi has bold plans to dramatically shorten the time it takes to advance a new therapy.
"We believe that by using our platform that these companies will be able to shrink the time it takes to go from idea to FDA Phase II clinical trials from 10 years to two years," Singh said.
Slideshow: Big Data Biopharma Forum