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It’s long been known that many serious diseases—including heart disease, asthma, and many forms of cancer—run in families. Until fairly recently, however, medical researchers have had no easy way of identifying the particular genes that are associated with a given malady. Now genome-wide association studies, which take advantage of our ability to sequence a person’s DNA, have enabled medical researchers to statistically correlate specific genes to particular diseases.
Sounds great, right? Well, it is, except for this significant problem: to study the genetics of a particular condition, say heart disease, researchers need a large sample of people who have the disorder, which means that some these people are likely to be related to one another—even if it’s a distant relationship. This means that certain positive associations between specific genes and heart disease are false positives, the result of two people sharing a common ancestor rather than their sharing a common propensity for clogged coronaries. In other words, your sample is not truly random, and you must statistically correct for “confounding,” which was caused by the relatedness of your subjects.
This is not an insurmountable statistical problem: there are so-called linear mixed models (LMMs), which are models that can eliminate the confounding. Use of these, however, is a computational problem, because it takes an inordinately large amount of computer runtime and memory to run LMMs to account for the relatedness among thousands of people in your sample. In fact, the runtime and memory footprint that are required by these models scale as the cube and square of the number of individuals in the dataset, respectively. So, when you’re dealing with a 10,000-person sample, the cost of the computer time and memory can quickly become prohibitive. And it is precisely these large datasets that offer the most promise for finding the connections between genetics and disease.
Enter Factored Spectrally Transformed Linear Mixed Model (FaST-LMM), which is an algorithm for genome-wide association studies that scale linearly in the number of individuals in both runtime and memory use (see FaST linear mixed models for genome-wide association studies). Developed by Microsoft Research, FaST-LMM can analyze data for 120,000 individuals in just a few hours, whereas the current algorithms fail to run at all at even 20,000 individuals. This means that the large datasets that are indispensable to genome-wide association studies are now computationally manageable from a memory and runtime perspective.
With FaST-LMM, researchers will have the ability to analyze hundreds of thousands of individuals to look for relationships between our DNA and our traits, identifying not only what diseases we may get, but also which drugs will work well for a specific patient and which ones won’t. In short, it puts us one step closer to the day when physicians can provide each of us with a personalized assessment of our risk of developing certain diseases and can devise prevention and treatment protocols that are attuned to our unique hereditary makeup.
—David Heckerman, Distinguished Scientist, Microsoft Research Connections; Jennifer Listgarten, Researcher, Microsoft Research Connections
One of the core missions of Microsoft Research Connections is to support the creation of software tools that advance data-intensive science, especially those tools that are judged praiseworthy by their creators’ peers. With this in mind, we were pleased to present the first Microsoft Research Distinguished Artifact Award at ESEC/FSE 2011, the joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering.
This new, competitive award honors the most outstanding software tool submitted to the ESEC/FSE series of conferences. As explained in the call for submissions, the Distinguished Artifact competition is intended to reward creation of artifacts and replication of experiments. An Artifact Evaluation Committee was established to review the submissions and to formally recognize those artifacts that pass muster and fast-track them for additional presentation. Artifacts deemed especially meritorious were singled out for special recognition in the proceedings and at the conference, and the creators of the best artifact received a prize of US$1,000, a handsome certificate, and a memento from the Pacific Northwest, the last a reminder of their friends at Microsoft Research Connections in Redmond, Washington.
Professor Andreas Zeller (left) presents the award to Jérôme Vouillon (right) while Christian Bird (center) of Microsoft Research looks on.
So, are you wondering which artifact took home the big prize? Well, wonder no more: the winning artifact was Coinst, an application based on the paper “On Software Component Co-Installability,” by Jérôme Vouillon of CNRS and Roberto Di Cosmo of Université Paris Diderot and INRIA. Coinst resolves the common and frustrating problem of finding co-installation conflicts; what’s more, it does so in a scientifically strong manner (by using a theorem prover), and it runs very effectively. Coinst not only satisfies all the expectations established in the paper, but exceeds them in several ways: by working quickly, performing better than presented in the paper, finding real errors in installed systems, and rapidly identifying frustrating problems that the reviewers have encountered in their own computer usage.
Professor Andreas Zeller of the University of Saarland, the initiator of the award, spoke about its importance, noting that "Far too often, researchers publish their results, but keep their data and tools for themselves. In the long term, this hurts science, because one cannot reproduce results or build on the achievements of others. Vouillon and Di Cosmo make their tools widely available and usable, providing value not only for other researchers, but for everyone. This way, they act as role models for the research community. With this award, we are proud to recognize their extraordinary efforts."
The winners themselves had this to say: “Free software components are growing at an astonishing pace, and it is important to identify quality issues quickly. We show how to efficiently extract from huge collections of free software a compact representation that quickly identifies component incompatibilities that would go otherwise unnoticed for a long time. We are thrilled to provide a tool based on a sophisticated algorithm that has been machine checked and that paves the way for the large-scale analysis and visualization of software component collections."
Well done, Jérôme and Roberto.
—Judith Bishop, Director of Computer Science, Microsoft Research Connections
Working as an intern at Microsoft has many benefits, but a vacation in Hawaii is not usually one of them. This year, summer interns had an opportunity to work on exciting new mobile technologies, while competing with their peers for an all-expenses paid trip to one of the Hawaiian islands. Microsoft Research Connections—in partnership with Microsoft Research’s Mobile Computing Research Center and Windows Phone—hosted a first-of-its-kind intern competition: Hawaii XAPFest. The competition was open to all U.S.-based Microsoft interns. The challenge: develop Windows Phone apps by using Project Hawaii services and that make use of new consumer features coming in the next version of Windows Phone, code-named “Mango.”
All participants were trained in the key Windows Phone development areas to provide them with necessary background to complete the challenge. The training included a series of lectures about relevant Microsoft technologies, such as Microsoft Silverlight, XNA, Project Hawaii services, and Windows Azure. Armed with this knowledge, each participating intern developed a Windows Phone app for submission to the evaluation committee comprised of researchers and developers from Microsoft Research and Windows Phone.
The final round of XAPfest judging took place on August 9, when finalists presented their projects to a panel of judges comprised of Microsoft executives. Each finalist was required to present their project to the judging panel and provide a live demonstration of their app. The judges selected the top four projects based on their creativity, presentation, use of Project Hawaii, and use of features in the next version of Windows Phone.
Top Award Winners
The grand-prize winner was Julia Schwartz, a second-year graduate student at Carnegie Mellon University and an intern with the Microsoft Research Human Computer Interface (HCI) group. Julia’s app, “Headshot,” uses facial detection and audio feedback to make it simpler to get the perfect self-portrait every time. Julia’s prize for this victory is a trip for two to Hawaii. Congratulations, Julia!
The top three runners up were:
All of the presentations we saw this year were very impressive, which made it tough to pick a final winner. The quality of work we saw from our participants demonstrates the innovation we continue to see with Windows Phone. I’m pleased to say I received overwhelmingly positive comments from contestants, who shared that they had a great time participating in this unique, exciting competition. Of course, the most excited of all is Julia, who started out working with Project Hawaii, and is now set to take off and see the “real” Hawaii!
—Arjmand Samuel, Senior Research Program Manager, Microsoft Research Connections