The cost of sequencing the human genome has plummeted. But how will this benefit doctors and patients?

Biomedical researchers hope to harness data about the human gene pool to usher a new era of “personalized medicine,” which could lead to a highly targeted treatment of disease.

Syapse is a Palo Alto, Calif.-based startup that claims to be one of the largest repositories of human molecular profiles. It has raised $3 million today.

The startup is on a mission to bring human genetic data out of the lab, and into the hands of doctors. ”Health care traditionally has been a field of observational science,” said CEO Glenn Winokur in an interview. “With the advent of being able to measure more complex components of the human body through sequencing, we are able to have more precise diagnosis and treatment.

“Many feel this [personalized or precision medicine] is the future of health care,” he continued, but revealed that the problem is “very challenging” as the data is “new, voluminous, and complex.”

The Social+Capital Partnership led the first round of funding. It’s a venture fund launched by a former vice president at Facebook, Chamath Palihapitiya, who will join Syapse’s board.

Syapse is one of a growing number of Silicon Valley startups that seek to remove some of the guesswork out of medicine.

Randy Scott, a cofounder and CEO of InVitae, said he is working with Syapse on a shared goal of “bringing genomics into everyday clinical practice to truly enable personalized medicine.” Syapse provides the software infrastructure to “connect genomic and clinical information across millions of individuals,” he said in a statement.

Scott is the former CEO of Genomic Health and is an important figure in the genomics sector. He has poured his personal resources into the creation of a dream diagnostic test to help geneticists look broadly for rare, inherited genetic disorders.

Related: Read about the efforts of genome entrepreneurs to sequence the DNA of millions of people, and learn more about the human gene pool.

Syapse is currently sequencing 3,000 patient genomes per quarter. It is working with laboratories and clinics using the latest sequencing technologies to diagnose and treat patients. Customers include Foundation Medicine and Genapsys.

Its flagship cloud-based technology, launched last summer, finds use from labs and companies as a way to deliver diagnostic reports. It generates a full report for physicians, which Syapse electronically delivers.

Winokur said by email that the funding will help expand the team, primarily in the hiring software developers. He also hinted at an additional product, which will be geared at delivering “genomics based solutions” to select hospitals, meaning that patients will someday receive drug therapies based on their individual molecular blueprint.

The company was founded in 2008 at Stanford University by a team of entrepreneurs and software engineers. It an undisclosed angel round, it received $1.6 million. Social+Capital’s limited partners include billionaire philanthropist and entrepreneur Eli Broad, and the Mayo Clinic, among others.

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This is a guest post by Jon Soberg, managing director at Blumberg Capital.

Venture capitalists picking up the Moneyball strategy. It’s a fascinating idea, and I know plenty of my peers are working on algorithmic approaches to better their craft and gain an edge. As I see innumerable pitches that leverage “big data” as a key aspect of a business, and read about all of the ways that big data is changing the world, it seems natural that it will infiltrate the world of venture capital. Whether it works or not remains to be seen.

As someone who has worked at both ends of the spectrum — as a CFA performing public equity analysis, and an early stage investor — this is more than just a passing interest for me. I pay close attention to data, and I firmly believe that people who understand the numbers better than their competitors, will ultimately win. In public equities, some groups have clearly found ways to outperform using algorithms over time periods.

Are algorithms the future of VC?

In VC, we know the stats; a large percentage of companies fail, and returns have lagged over the past several years (although some firms have continued to outperform). Is data analytics the answer as some have suggested? Is it key to success in our industry, which is clearly changing? I have to say no, but will qualify my answer.

At the early stage where I invest, I am often assessing a team with a product that is not yet in the market, with no revenue, and with plans to hire some significant team members within the first year. Small data. There are very, let me stress VERY, few data points. And most of the data points that are there are qualitative, and there is plenty of noise. It hasn’t stopped me from building a few models along the way to see if I can glean some insight, but I can tell you what the models yield — garbage in, garbage out.

One might argue that algorithms can help predict markets, and I believe that could be true, but the size of the market opportunity for most companies is defined not by the actual size of the market, but by the team’s ability to address the market. It is SAM, not TAM. Let’s say a model detects that a market is developing, can the model help select the team that will win in that market? Revenue and growth are tightly coupled with a team’s ability to understand the needs of their customers and to fulfill those needs. It comes down to the team. I have yet to see a model that can tell me that a Mike Lazerow, Jason Goldberg, or Joe Lonsdale are better at figuring out their markets than others, but I can tell you that if you meet with any of them in person, you will know.

Early-stage vs. later-stage investing

That said, I do think they can be helpful in some ways, especially in later stage investments. One of my toughest challenges is assessing opportunity cost. If an algorithm can help me decide which of several investments has the best potential, then it can be valuable. But, in my experience, the value is directly linked to the maturity of the company, as small data becomes bigger data. And the breadth of the comparison set is critical to get a good signal; any bad data in these models can really skew results.

One algorithmic approach that I believe has potential is employed by Correlation Ventures. They analyze investments made by other VC’s using their proprietary algorithm, and join some of the rounds. Just the fact that they are leveraging the work done by other VC’s gives them an inherent advantage in selection — they add another layer to existing analysis.

Data won’t help us find the next Zuckerberg

The combination of human and algorithmic analysis is the best way to use data analytics. But data analytics won’t help us score the home run hits that are absolutely essential. The Moneyball approach simply doesn’t work because hit rates are not high enough to manufacture returns by getting on base. The key is swinging hard every time you step up to the plate — a Facebook home run is like hitting the ball OVER McCovey Cove and getting 10 runs for it.

So when I look at how data analytics will impact VC, do I realistically think algorithms will give me a competitive advantage? No. These are not liquid markets, and there are very few good data points. Will it help with due diligence and adding a few data points for my analysis? Maybe. Will it help identify target markets? Potentially. Will it make me a better investor? Doubtful. Will it help me win deals versus our competitors? No. Will it help raise money? To be determined.

To quote one of my mentors, Wharton Faculty member Dave Pottruck, “Go with your gut, but always crunch the numbers first.” But in the absence of all that much valuable data, VC’s need to rely on old school-hustle, homework, and instinct.

Jon Soberg is a Managing Director at Blumberg Capital, where he invests in early stage companies, specializing in FinTech, SaaS, and eCommerce. Prior to joining Blumberg Capital, Jon has been a serial entrepreneur and senior executive in multiple companies including Ditech, Broadband Digital Group and Adforce, which had a highly successful IPO.  

A CFA Charterholder and adjunct faculty in the Wharton Marketing Department, Jon earned a B.S in Engineering from Harvey Mudd College, an M.S. in Engineering from Northwestern University, and an MBA in Entrepreneurial Management and Marketing from the Wharton School, where he is a Palmer Scholar.

Top Image via Shutterstock

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HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

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Positing that data analysis on a grand scale can solve the nation’s most critical challenges, the Obama Administration has kicked off a big data initiative with $200 million in commitments from six federal agencies.

The White House Office of Science and Technology Policy (OSTP) is collaborating with the Department of Defense (DoD), National Science Foundation (NSF), Nation Institutes of Health (NIH), Department of Energy (DoE), U.S. Geological Survey (USGS), and the Defense Advanced Research Projects Agency (DARPA) to better organize and understand massive volumes of digital data for the nation’s well-being, the Administration announced Thursday.

“In the same way that past Federal investments in information-technology R&D led to dramatic advances in supercomputing and the creation of the Internet, the initiative we are launching today promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security,” assistant to the President and OSTP director Dr. John P. Holdren said.

Each of the agencies involved are committing resources to developing technologies for big data insight and discovery. DARPA and the DoD, for instance, are specifically focusing on analytics, tools, and algorithms for improving national security and optimizing warfare tactics.

The government is the latest investor in the big-data movement, which has taken off in the technology sector as service providers raise large rounds or get snatched up for tools that dive deep into corporate data pools.

Photo credit: Yampyank/Flickr

“We want to do to data analysis what Apple has done to computers: Make data so intuitive that anyone can gather insights from tons of data,” said Metalayer Chief executive Chris Burrage in an interview with VentureBeat.

MetaLayer helps you gather insights and make visualizations, also known as infographics, from massive amounts of data on the web. The company has poised itself as the “Photoshop of data,” mainly because you can drag and drop sets of data onto one another to help make sense of datasets with graphs and diagrams, much like adding layers to a Photoshop image. You can import pictures, statistical figures, Twitter feeds, documents, and news feeds into MetaLayer’s dashboard and then analyze them for trends or other insights. Metalayer aims its service at data analysts, non-profits, and journalists who may not have the immense knowledge needed to analyze huge amounts of data, nor the money to pay for a robust system to do it for them.

Now the company is launching a community platform called Delv, where infographics can be vetted and shared. People can pick apart the data that others have put together, and point out flaws or ask questions. Down the road the company hopes the platform will become a place to share data visualizations created by “real people”, not statisticians and professional analysts.

“Our new community project platform, Delv, is for people to come delve into the information analyzed on our site,” said Burrage. “People can look at infographics and other visualizations that community members have created, see the raw data that went into making them, mix up the data, and point out weak points.”

The company graduated in December 2011 from DreamVenture’s incubator and launched its dashboard, which is still in beta, in November 2011. MetaLayer also offers two APIs, imgLayer and dataLayer. ImgLayer takes words and objects from images and makes tags out of them, and dataLayer adds context to social data. Right now all the services are free, but in the future MetaLayer plans to charge for premium services such as private infographics that aren’t automatically shared with the community. It also plans to license infographics that are created by members at some point.

MetaLayer is based in Philadelphia and is run by a three-man team.

July 9-10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Social analytics company SocialNuggets is releasing its first free analytics portal Wednesday. The site provides free data analysis for the tablet industry, based on millions of tweets, blog posts and other online content.

Other portals focused on different industry verticals will follow.

Processing and making sense of this kind of data can take a lot of manpower. SocialNuggets addresses that with its language-processing and text-analytics engines, which its customers can employ for decision making.

“Our focus is exclusively on the consumer electronics industry,” said chief executive R. Paul Singh.

The tablet portal showcases data on the tablet industry, intended to help consumers and companies alike plan and buy. To find tablet data, SocialNuggets scoured upwards of one million conversations occurring on blogs, forums, social media, etc. over a six-month period. From there, it measured sentiments, counted the number of conversations, and more. Here are a few “nuggets” SocialNuggets uncovered:

1. The top three uses for tablets are shopping, business and gaming. Business occurs mostly on the Apple iPad and Blackberry Playbook.
2. Other than Twitter, most tablet conversations occur on Apple-themed forums.
3. Consumers find HP Touchpad’s price most favorable, while the most negative comments were directed towards the Blackberry Playbook.
4. Apple dominates the mindshare of consumers regarding tablets. Samsung and Motorola come in second and third respectively.

More verticals are to come. “Every month you will see brand new data that will be put on the public portal,” said Singh.

Sentiment analysis is particularly important for analysis portals. Singh, who is confident in his sentiment engines, used the example of “long battery life” versus “long charge time.” The phrases are very similar, but one is positive and one negative. Sentiment engines like those used by SocialNuggets are designed by humans to evaluate words in context, ultimately reflecting in more accurate data analysis. Additionally, SocialNuggets uses charts to display data in the portal. Both of these require some human input by SocialNuggets staff — they’re not completely automated.

“It’s not as much manpower as it might appear,” he explained. “When we train a market the first time, it takes about 2 weeks. Once that is done, then that person isn’t working on that market except for periodic checks.”

As far as charts go, the portal does produce standard graphs in iChart, with a little human work behind the scenes for beautification.

In order to monetize its free analysis, SocialNuggets makes money from its premium data portals, which provide company-specific data for a fee. SocialNuggets most recently signed the Consumer Electronics Association as a customer this past June.

Competitors like Radian6 and ViralHeat perform similar functions, but provide a “firehose of data,” according to Singh and don’t provide enough accurate analysis, he says. ViralHeat recently announced a Human Intent program.

“I know that half of my advertising doesn’t work. The problem is I don’t know which half.” – John Wanamaker

For more than 60 years, this well-known statement about advertising has been an accepted truism. Roughly five years ago, though, there was a sea change in marketing in both the tools and attitudes of its practitioners.

Armed with the ability to measure results at a fine-grained level, marketers started making more data-driven decisions about their advertising. The ability to measure was catalyzed by online advertising, a uniquely traceable medium for marketing. Accessibility to simple but sophisticated analysis tools like Google Analytics only increased marketers’ abilities to be data-driven. Not only could they trace where their dollars had been spent, but also the effectiveness of each program, message, source and landing page from influence through to purchase. The next generation of marketers was born: data-driven marketing analysts.

Engineering teams, meanwhile, have spent the past 25 years building software, shipping it on a CD (until relatively recently), sending it into the field and only received limited feedback on what does and doesn’t work. Product development has primarily been about what’s next, not about what’s working. While big picture feedback may have made it back to the development team, the fine-grained detail of usage in the system was completely lost.

Just like advertising, development is about to change.

With the move to web-based Software-as-a-Service (SaaS) systems that can be instrumented to measure usage (Google Analytics, MixPanel, etc.), product teams now have access to rich data on how an application is really being used. Strangely, though, most product development teams still don’t use this information (let alone instrument their applications). Why?

Just as in marketing, while measurement tools exist, there is still a cultural gap between the data-driven decisions these tools allow and the way departments think and are organized. Part of this comes from the clichéd (but often true) story that most product development organizations are not truly customer-facing.

Product management was designed to be the proxy for constituencies and what they want. But inevitably product management focuses on the future – what those users want next, not their opinion of the current application. We primarily still develop software through anecdote not data.

Instrumentation of application usage should be a core discipline of every engineering team member. Each feature developed should be measured, and the developer should own the measurement and analysis of how successful it was.

In an Agile SaaS model, your greatest strength is your ability to adapt quickly. Similar to poorly performing marketing programs that can now be adjusted quickly, features that aren’t accomplishing their goals should be triaged and updated or removed.

Before features are developed, software developers should determine how their success will be measured. In the same way marketing shouldn’t spend $50,000 on a marketing campaign without tracking results, development teams shouldn’t spend similarly on expensive development resources building and testing a feature without consideration of how to measure its success.

It’s hard to remove parts of an application that some people on the development or product management team don’t “feel” are working well. The conversation becomes emotional and subjective and no-decision ends up being the decision. Thus, we all end up with a big bundle of feature cruft, lots of technical debt, and an application that makes development and testing regression cycles longer every time.

The lingering question is, “what should we measure?” The quick answer, like in marketing, is start with everything. You simply don’t know which metrics truly represent your users’ experience until you start mining data. As you examine results, you’ll start to get a better picture of what matters and what doesn’t. Once your team understands the basics of usage measurement, they’ll start to see the dynamics of how changing the application affects outcomes. This is similar to how a small change to a landing page can change lead conversion rates dramatically.

Once your team is comfortable with the building blocks, elevate the development metrics to higher-level usage outcomes. These will of course depend a lot on what the goals of your application are – discussions that will lead you back into the product management domain. Just like marketing outcomes start to turn into sales outcomes, you will start to align your development team and your product management team.

Fundamentally, this approach puts power back into the hands of the development staff. For 25 years developers have been screaming, “just give us problems and we’ll solve them,” but we’ve tied their hands with little data to allow them to know what to do. The revolution that’s coming is for developers to get the data themselves, have a seat at the table, and recommend changes to the application based on results.

Data empowers – and it will always trump anecdote.

The secretive data-analysis startup, based in Palo Alto, Calif. and backed by early Facebook investor Peter Thiel, has suffered a number of blows to its public image of late. The most recent is the settlement of a lawsuit filed by rival i2 Group, based in McLean, Va., over accusations that Palantir employees fraudulently obtained i2 software and used it to design competing products.

Since Palantir touts itself as the product of fraud-detection technologies pioneered at PayPal, the payments startup Thiel cofounded, those charges present ironies, as i2′s lawyers eagerly pointed out in their initial complaint.

Separately, Palantir CEO Alex Karp issued a public statement apologizing for his company’s role in preparing a plan for Bank of America to strike back at Wikileaks, the Internet-based nonprofit group famed for obtaining and releasing sensitive documents into the public domain. The company also placed employee Matthew Steckman on leave after hackers released emails showing he was involved in preparing a similar plan for the U.S. Chamber of Commerce to damage ThinkProgress, a pro-labor publication.

Mitch Derman, an i2 spokesman, and Palantir general counsel Matt Long both declined to comment on the settlement of their companies’ dispute, citing an agreement in the settlement not to discuss the case further.

i2′s original complaint, however, makes claims that are embarrassing to both companies. The short version: According to i2, Palantir employee Shyam Sankar obtained i2 software representing himself as a principal of SRS Enterprises, a company registered under the names of his parents in Florida. Sankar then allegedly proceeded to analyze i2′s software and use the results to develop tools to import data from i2′s software for use in Palantir’s analytical tools. (For its part, Palantir responded by alleging that i2 had “unclean hands” because it purportedly used information about Palantir’s products obtained through a company i2 acquired.)

Even lodging this complaint posed an image problem for i2: The company said the claimed fraud went on for four years, right under its nose. Derman, the i2 spokesman, did not directly answer a question about whether i2 used its software in its own operations to detect fraudulent customers. Instead, he noted that i2 has a wide range of customers in law enforcement and other parts of the public sector.

One could make the same critique of Palantir. In a statement provided by Long, the company said that the kind of dirty tricks proposed against Wikileaks and ThinkProgress were “contrary to Palantir’s ethics, culture and policies.” If Sankar and Steckman did conduct themselves improperly, and Karp, the Palantir CEO, had to apologize for the company’s actions in the Bank of America brouhaha, how effective is Palantir at detecting breaches of its own policies before they burst into the public eye? And should its public-sector customers, some of them engaged in critical national-security activities, trust it?

Here’s i2′s original complaint against Palantir:

i2 v. palantir – 080910 http://d1.scribdassets.com/ScribdViewer.swf

And here’s Palantir’s response:

http://viewer.docstoc.com/
i2 v. Palantir: Palantir’s Answer to Complaint

To measure trends in Facebook’s status updates, data scientist Lars Backstrom looked at words or phrases in terms of the ones that grew most in status updates compared to last year. And the result? “Highs and lows of world events that started a global conversation, new uses of language online, and the sharing of popular culture between friends,” reveals Backstrom.

In similar fashion, Twitter posted its own top ten trends. The complete trend lists, including breakdowns of categories, can be found here.

As a comparison, both trend lists match up particularly well. The FIFA World Cup, Inception, Haiti earthquake, iPad, and Justin Bieber, seem to have found their way onto both. Missing surprisingly from Facebook’s list are the Oil Spill and Pulpo Paul the Octopus, which may be due to the fact that they were largely termed in 2010 itself, and the equally surprising topper was “HMU” — short-hand acronym for “hit me up.”

Worth noting is that while Facebook’s list measured trends relative to their weight in 2009, Twitter’s looked at 2010 alone. Also, Twitter maintains a trending list visible to all its users, which may more or less perpetuate the trends themselves. In contrast, Facebook’s status updates are mostly implicit and spread naturally from user to user.