Kristin Tolle on biomedical initiatives at Microsoft Research

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Kristin Tolle is the Senior Research Program Manager for Biomedical Computing for External Research in Microsoft Research. Projects run the gamut, she says, from "bench to bedside". In this interview she discusses two major biomedical initiatives: Cell Phone as a Platform for Health Care, and Computational Challenges of Genome Wide Association Studies.



Kristin Tolle

JU: Give us a sense of the kinds of biomedical projects you're working on internally, as well as those you're working on with external partners. I spoke with George Hripcsak, one of the researchers awarded a grant under the Computational Challenges of Genome Wide Association Studies (GWAS) program, and I know there are others involved there and in other programs as well. I'm interested in what Microsoft brings to the table in terms of helping these folks out with their computational and data management challenges, and also what kinds of things Microsoft learns from these engagements.

KT: The different programs inside of External Research run the gamut from the devices and mobility space, for home health care and elder care, all the way to genome wide association studies. So, we fund projects all the way from bench to bedside.

Because we're a software company, we'll focus on the IT parts, and there's a reason for that. These are often the parts that don't get funded elsewhere, or only get funded sparsely. Our purpose for going into medical funding was to fill those gaps.

JU: And why do you think those gaps exist?

KT: I think it's a misperception, by a lot of the funding agencies, that either something doesn't fall into their area, or that it's not as important as the actual research being done.

The problem is -- and this is why we're funding this area -- you cannot do medical research without computing. You just can't.

JU: Of course not.

Areas that we've funded...well, the biggest RFP we ran this year was Cell Phone as a Platform for Healthcare, and that was 1.4 million dollars toward trying to reach rural and underserved communities with retro technologies like cellphones and televisions, because those are ubiquitous.

JU: Oh, absolutely. I've spoken to Joel Selanikio, who was recently awarded a MacArthur Grant to use handheld devices for field data collection in the third world. It's a huge opportunity, though as you say it's the sort of retro technology that doesn't make people's eyes light up in Silicon Valley, they just don't see the opportunity the same way.

KT: It's true that they don't. But interestingly we've got a lot of researchers in-house, whether we're talking about that situation or about genomics, who have a keen interest in working in these areas. So for example, we gave Fone+ devices to a couple of the people who were winners of that award. The Fone+, which was developed by Microsoft Research Asia, is a phone that sits in a cradle, it's got RGB out to a television set, and USB input ports for mouse, keyboard, etc. So basically it enables your phone to work like a PC.

Now the beauty of this is, if you hook that up to a microscopy device that can do instant visualization of blood cells, determine whether or not somebody has malaria, and display that on a television screen, you've now just set up a lab for doing microscopy anywhere in the world there's a TV and a cellphone.

Another example is something we did with Washington University in St. Louis. They're developing low-cost ultrasound probes. Same thing. They're USB out, and designed to work with laptops, but now with the Fone+ you can plug it into this little cradle and now you've got an ultrasound anywhere in the world where there's power, a TV set, and a cellphone. You can even control the ultrasound device from the phone itself, it's just an amazing technology.

So that's an example where Microsoft Research has developed a technology that facilitates providing health care to rural communities. Although it wasn't initially designed for that, it was initially designed for education. But I took it and sort of twisted it..

[laughs]

...and said, hey, that'd be really good for the cellphone as a platform for healthcare project. I got them to give me a bunch of phones and cradles, and started sending them out to the researchers who had won awards for the RFP I ran this year.

JU: What kinds of things have you heard back?

KT: We've only funded them six months ago, so we won't see results probably until sometime next year. But I've actually seen a demo with the Fone+ and the ultrasound unit already working, so that was impressive. Washington U. is ahead of the game, I'd say.

JU: Are the folks you funded to do these things expected to bring technical chops to the table, in order to extend these devices? Are you working with them to provide support?

KT: Yes, we expect them to bring something to the table. And the ones who win the awards have superior technology. We had 145 people submit to the cellphone as a platform for healthcare. We'd originally planned to fund a million dollars, so that's about 10 projects, but we had to extend it to 1.4 million because we wanted to get to 10% acceptance rate. But even that, for us, is generally fairly low. Usually our acceptance rates are much higher. But we were just bombarded by people trying to come up with solutions for this space.

It was disappointing to only be able to do 14 proposals because when I looked back through them, I'd say 85 were fundable and on the bubble. Isn't that terrible? You wish you could do more.

So, I know you've talked to George about the genome wide association study, but I'd like to head in that direction in terms of some other things we bring to the table.

When I went looking inside MSR for collaborators, what I learned was that there's a plethora of them. It's kind of surprising we hadn't been funding this area before, and it's no surprise to me now that it's become a strong pillar of funding for our organization. In fact we've trimmed a lot of other programs and will be focusing a lot on the healthcare space this time around.

When I went hunting for collaborators I had no trouble finding them, even though I was new to the team, and that was because people consider healthcare the killer application for what they're working on.

But we also had a rich group -- you know, we have a couple of MD/PhDs working here, Eric Horvitz and David Heckerman -- and David does a lot of work in the development of vaccines for HIV and malaria. But he's branching out now into this GWAS area. So he's been looking at Lou Gehrig's disease...

JU: We should define the term GWAS, for people who aren't familiar.

KT: Sure. Genome wide association studies look across the genome to find if there are particular genes implicated in disease. That's one side of it. Another side is looking across the genome to check for reactions to different pharmaceutical agents.

In simplest terms, these studies are what will deliver on being able to provide personalized medicine for all of us in the future.

JU: Exactly, because it's a scan of an individual's complete genome, looking for markers and correlations.

KT: Absolutely right, that's correct. And I believe it will really deliver on personalized medicine for the masses.

And the thing is, it's happening already. We've got 23andMe popping up, Navigenics, people are going to start using their genomic information to make informed decisions about the type of healthcare they receive. They'll be taking that to their doctors and assuming they'll be able to work with it.

So we need to push the IT component of this down so that doctors have access to the information and know how to utilize it. Right now, that's the clinical gap between the research that's taking place in this area and the doctors who are performing the services needed.

JU: So in this case you funded about a half dozen individuals to look into different aspects of this GWAS research...

KT: ... yeah, very different...

JU: Right. So what do you hope will result from it?

KT: This was a new area for us, for Microsoft Research. We'd been dabbling in genomics for a while, but here we wanted to cast a wide net, find out what was going on out there, and find out if there were potential collaborations we could take from there.

When you find people whose work you can help facilitate, you form strategic collaborations with them to take that research to the next level.

Of course we bring a lot of resources to bear on this space. For example, the Microsoft Computational Biology Tools that we've published out on CodePlex, open source.

The other thing we bring to bear is a deep knowledge of machine learning and knowledge representation. And a number of researchers who've been working in general fields, but are now turning their attention to genomics.

A couple of new examples: John Winn, and also Christopher Bishop who literally wrote the book on machine learning and pattern recognition.

JU: This is a pattern I'm seeing often in these external partnerships. In all areas of science, as you say, scientists are necessarily becoming computational in the work they do, it's just the nature of the beast. But they don't necessarily have deep domain expertise in either algorithms or data manipulation and analysis. There are lots of folks at MSR who are deep in those areas, and who can effectively partner with these folks to move things forward.

KT: And it's not just that we have these underlying analysis and infrastructure technologies, we also have the human-computer interaction technologies to make that stuff usable for clinicians, or even the public themselves. So we've got people doing interesting work in how do you make something more understandable? How do you do machine translation across sex, age, status, education?

It's the same type of machine learning problem that you have with regard to going across language. You have to translate between languages, but you even have to translate within a language between different cultures, different demographics.

JU: What does Microsoft learn as a result of these collaborations, and what is Microsoft able to do with that?

KT: Well, the overall goal for External Research is to facilitate time to discovery, and to do so in a way that extends the arm of Microsoft Research. What we learn are which directions to move in. You know, we have publishing and tenure track promotion in Microsoft Research just as in academia. So if we can make our researchers more effective in reaching their goals to publish papers in Science and Nature, that's a fabulous thing. We've facilitated them and extended their reach.

There's also corporate responsibility here as well -- Microsoft, as a company, investing in areas that are important for the future. It's also important for us to keep abreast of the times, and the things taking place now. And finally, we learn things that we may incorporate into our products through tech transfer.

JU: It may be early days to talk about tech transfer from your biomedical projects, but I'd imagine one obvious outcome will be related to the kinds of devices that will be part of the HealthVault program, as sensors start to exist in people's homes, monitoring their vital signs, and transmitting them to the cloud.

KT: Absolutely, there's no doubt about it. The more that we invest in applications, and in sensors that can feed HealthVault, the richer their offering becomes.

The other thing is that we feel we're helping the public become more knowledgeable about their own healthcare. I think that's a common goal we share with the Health Solutions Group, Peter Neupert's organization.

They have other goals as well. So for instance, they have Amalga on the clinical side, and also a project targeted at researchers, trying to take people through the literature search for drug discovery. We're working in conjunction with that. One of the projects we funded under GWAS was a system to predict possible adverse drug reactions based on genome wide association studies.

Then the Columbia project -- George Hripcsak, whom you spoke with -- he's creating tools for researchers to integrate clinical information into the genetic analysis. Well, George's project is being built on top of Amalga. So there's a lot of synergy with the Health Solutions Group. And that's not unplanned. When I was starting out I met with Peter Neupert, back when he had eight people in his organization, and I interviewed him to find out what areas we should be investing in for healthcare. I'd also visited various schools and talked with people in their biomedical programs to find out what they were investing in as well. Then I tried to identify areas that would be relevant both to Microsoft Research and the Health Solutions Group. So, it's not just serendipity.

JU: So your own background is in bioinformatics?

KT: Biomedical computing. I had to form a multidisciplinary PhD committee because my school didn't have a program for this, though they do now, at the University of Arizona. So I had to form a multidisciplinary committee to get a PhD focused on machine learning for healthcare, with computational linguistics thrown in. It was tough, but it was worth it.

JU: Natural language processing was part of your focus as a student?

KT: Absolutely. Although the systems I developed were much more broadly utilized by different organizations. In fact, homeland security has some of the code I developed, which they use to scan for terrorist activities. It was initially developed for the National Library of Medicine to scan through unstructured text and identify keywords for indexers, and also to create small indices so that you could search faster and more accurately for publications in PubMed and CancerLit and other digital libraries. But you could see there were other implications. In fact it's also been used by the Department of Justice to make correlations among police reports.

So it's a generic technology, but my piece of it was targeted toward healthcare, and that's where my background and interests have always been.

JU: Are there other areas you'd like to discuss?

KT: I think we've covered the two major ones. I see us really investing long term in the area of devices, sensors, body sensor networks, ubiquitous and pervasive computing. That'll be a fundamental theme going forward, because it's been one of the more successful areas that we've made investments in.

But I also see us keeping a strong eye on the "omics" -- proteomics, genomics, metabolomics, you name it.

A third important area, and I don't know if it will be short term or long term, which is to address the other thing we talked about, and I don't have an RFP in this, but machine translation for people to be able to understand health care documents.

The average person cannot go out on Medline and read the literature on their disorder.

JU: It is amazing, though, how much context people can assemble for themselves under pressure of intense need.

KT: No doubt about it. But it would be better if we could create facilitating interfaces that would enable people to more readily understand and interpret that information. There's a lot of it out there, it's information overload really, and if we could make it a little easier for them, that would be very valuable.

JU: I wonder how much of that will be done by machine translation, and how much by crowdsourcing various experts at various levels. I think probably both will happen.

KT: Yeah.

You know, another important area -- and I was a bit disappointed when we ran our GWAS RFP that we didn't get anything concrete in this area -- was data visualization for genome wide association studies.

I think that's because it's such a hard problem. These studies are computationally challenging as it is, there's a lot of data that gets generated. Then to visualize it, now you're adding another level of computational complexity such that it's already not realtime just looking at the data, then how do you take it to that next level of visualization? That's going to be an important emerging area going forward.

So for instance, we've been talking with the folks at Oxford about getting a Surface there for collaborative visualization of cancer pathology.

JU: Not just in this area, but in general, we are so underserved by our ability to make sense of large complex data.

KT: Yeah, and we have these cool technologies. I think the WorldWide Telescope could be redeployed in many environments, and I think healthcare is one of those killer applications. We were talking with the National Cancer Institute, and one of the things they'd like to do is take a slice out of the liver while the patient is still on the table and be able to zoom in and zoom out -- it's the same technology.

JU: It's a similar kind of thing. To the extent that we can, in different fields, standardize on data formats and define multidimensional data spaces, we can indeed have browsers and viewers for those spaces. What the Telescope does in its domain is create a browser for a web of astronomy data. So yes, we need to have browsers for webs of genome data, and all kinds of scientific data.

KT: We had a recent paper by Bongshin Lee, she's done a distance encoding tree -- she calls it Detective -- and it's a scalable visualization tool for mapping multiple traits onto evolutionary trees. So we're trying to tackle it inside Microsoft Research, but I was hoping to see more people outside MSR show interest so we could start forming interesting collaborations in that area.

JU: Well, this has been a lot of fun. I hope to follow up on some of those Fone+ applications, that sounds really inspiring.

KT: Yeah, that's the reason I've gone into this area. It is inspiring. There's not only corporate responsibility, there's personal responsibility for me as well, and that's why I like working in this particular space. It's genuinely gratifying to be able to make a difference in an area that, no question about it, is beneficial to society.

JU: Well you've landed in the perfect spot to do that, and it sounds like you're having a blast.

KT: Yes, I am. Well, thanks very much.

JU: Thanks Kristin.

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