In its fascinating cover article on the Search for a Parkinson's Cure, Wired  author Thomas Goetz lays out a new scientific research approach, a method that draws on Brin's algorithmic sensibility and Google’s storied faith in computing power—with the aim of accelerating the pace and increasing the potential of scientific research.

“Generally the pace of medical research is glacial compared to what I’m used to in the Internet,” Brin is quoted saysing. “We could be looking lots of places and collecting lots of information. And if we see a pattern, that could lead somewhere.”

Brin proposes to bypass centuries of scientific epistemology in favour of collecting data first, then hypothesize to find the patterns that lead to answers. He has the money, and as important, the algorithms to do it.

Can a model fuelled by data sets and computational power compete with the gold standard of research? Maybe: Here are two timelines—one from an esteemed traditional research project run by the NIH, the other from the 23andMe Parkinson’s Genetics Initiative. They reached almost the same conclusion about a possible association between Gaucher’s disease and Parkinson’s disease, but the 23andMe project took a fraction of the time — Rachel Swaby

Brin’s work at Stanford argues that given the right algorithms, meaningful associations can be drawn from all sorts of unconventional data sets—”student enrollment in classes, word occurrence in text documents, users’ visits of Web pages, and many more.” It’s not a stretch to say that our experiences as patients might conceivably be the next item on the list.

Google Flu Trends  makes use of syndromic surveillance, usually involving drugstore checkingfor cold medicine purchases, doctor’s offices for diagnoses, but because acquiring timely data can be difficult, syndromic surveillance has tended worked better in theory than practice.

By looking at search queries, Google researchers are able to analyse data in near real time. Indeed, Flu Trends can point to a potential flu outbreak two weeks faster than the CDC’s conventional methods, with comparable accuracy.

“It’s amazing that you can get that kind of signal out of very noisy data,” Brin says. “It just goes to show that when you apply our newfound computational power to large amounts of data—and sometimes it’s not perfect data—it can be very powerful.”

The same, Brin argues, would hold with patient histories. “Even if any given individual’s information is not of that great quality, the quantity can make a big difference. Patterns can emerge.”

Increasingly scientists,  those with a background in computing and information theory, are starting to wonder if that model could be inverted. Start with tons of data, a deluge of information and then searching for patterns and correlations?

It's what  Microsoft researcher Jim Gray, called the fourth paradigm of science an evolution away from hypothesis, toward patterns. Gray predicted an “exaflood” of data would overwhelm scientists unless they reconceived their notion of the scientific process and applied massive computing tools to engage with the data.

Bill Gates wagers $10m on the fourth paradigm, by investing in Schrödinger  the Portland firm using massive computation for fast simulation of the trial and error of traditional pharmaceutical research.

Former chair and CEO of Intel Andy Grove has  called for a “cultural revolution” in science, modelled on the tech industry’s penchant for speedy R&D. Diagnosed with Parkinson he is quoted: “After 10 years in the Parkinson’s field, we may finally have three drugs in Phase I and Phase II trials next year—that’s more than ever before. But let’s get real. We’ll get the results in 2012, then they’ll argue about it for a year, then Phase III results in 2015, then argue about that for a year ... The whole field is not pragmatic enough. They’re too nice to themselves."

So, with the cooperation of the Parkinson’s Institute, the Fox Foundation, and 23andMe, Brin has proposed a new development cycle, contributing $4m to fund an online Parkinson’s Disease Genetics Initiative at 23andMe: 10,000 people who’ve been diagnosed with the disease and are willing to pour all sorts of personal information into a database.

Volunteers have DNA extracted and analysed. That information is matched up with surveys that extract hundreds of data points about the volunteers’ environmental exposures, their family history, disease progression, and treatment response.

Questions go from mundane (“Are you nearsighted?”) to perplexing (“Have you had trouble staying awake?”) in the attempt to create the always-on data-gathering project that Brin believes could aid all medical research - no grand unified theory but a lot of data.

“Traditionally, an experiment with 10 or 20 subjects was big,” says the Parkinson’s Institute’s (right) Dr William Langston  “Then it went up to the hundreds. Now 1,000 subjects would be a lot—so with 10,000, suddenly we’ve reached a scale never seen before. This could dramatically advance our understanding.”

Perhaps this is the approach that researchers of the 'Glasgow Effect' need to pursue to find the pattern's flaws.