Genetic information for chronic disease prediction

Michael A. Grasso, MD, PhD
University of Maryland School of Medicine

1:00pm Friday 23 September 2011, 227 ITE

Type 2 diabetes and coronary artery disease are commonly occurring polygenic-multifactorial diseases, which are responsible for significant morbidity and mortality. The identification of people at risk for these conditions has historically been based on clinical factors alone. However, this resulted in prediction algorithms that are linked to symptomatic states, which have limited accuracy in asymptomatic individuals. Advances in genetics have raised the hope that genetic testing may aid in disease prediction, treatment, and prevention. Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We present an overview of genetic issues involved in polygenic-multifactorial diseases, and summarize ongoing efforts use this information for disease prediction.

Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine, and an Assistant Research Professor of Computer Science at the University of Maryland Baltimore County. He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, and the William Beaumont Medical Research Honor Society. He completed a residency at the University of Maryland School of Medicine, and currently works in the Department of Emergency Medicine at the University of Maryland Medical Center. He has been awarded more than $1,200,000 in grant funding from the National Institutes of Health, the National Bureau of Standards and Technology, and the Department of Defense, and has authored more than 35 scholarly papers and abstracts. His research interests include clinical decision support systems, clinical data mining, clinical image processing, personalized medicine, software engineering, database engineering, and human factors. He is also a semi-professional trumpet player and is interested in the specific medical needs of performing artists, especially instrumental musicians.

Host: Yelena Yesha

Many Google+ users have been reporting frequent notices about new followers that they don’t know and appear to be attractive young women. The suspicious followers have minimal profiles and no posts. These are obviously false accounts being created for some yet unknown purpose, but how can one prove it?

I just got a notice, for example, that Janet Smith of Philadelphia is following me. Now Janet Smith is a common name and Philadelphia is a big place — there are probably hundreds of people who live in the Philadelphia area with that name. The 990 other people she’s following seem like a pretty random bunch, though I do know many and have more than a few in my own circles. Most seem to have a fair number of followers.

So there is not much to go on other than her profile image. This is a great use for Google’s new image search. I dragged the picture into the image search query field and Google identified its best guess for the image as Indian actress Koyel Mullick. Sure enough, if you search for images with her name, the precise Janet Smith image is result number 15.

Of course, there are still some subtle issues. This is just one kind of false profile — one created for one identity but using an image from a different one. It’s common on most social media systems, including G+, for some people to use a picture of someone or something other than themselves. But it’s obvious to a human viewer that using a picture of a rabbit, Marilyn Monroe or the mighty Thor on your profile is not meant to deceive. It will be challenging to automate the process of discriminating the intent to deceive from modesty, homage or an ironic gesture.

The First Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one-day event to be held at the Johns Hopkins University in Baltimore on Friday, 23 September 2011. Its goal is to bring together students taking computational approaches to speech, language, and learning, so that they can introduce their research to the local student community, give and receive feedback, and engage each other in collaborative discussion. Attendance is open to all and free but space is limited, so online registration is requested by September 16. The program runs from 10:00am to 5:00pm and will include oral presentations, poster sessions, and breakout sessions.

Stanford is experimenting with an interesting idea — offering some of their most popular undergraduate computer science courses online for free and simultaneously with their regular offerings. An AI course was announced several weeks ago and now there are similar offerings for databases and machine learning. These are taught by first rate instructors (who are also top researchers!) and are the same courses that Stanford students take.

• “A bold experiment in distributed education, “Introduction to Artificial Intelligence” will be offered free and online to students worldwide during the fall of 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford’s introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams.”
• “A bold experiment in distributed education, “Introduction to Databases” will be offered free and online to students worldwide during the fall of 2011. Students will have access to lecture videos, receive regular feedback on progress, and receive answers to questions. When you successfully complete this class, you will also receive a statement of accomplishment. Taught by Professor Jennifer Widom, the curriculum draws from Stanford’s popular Introduction to Databases course.”
• “A bold experiment in distributed education, “Machine Learning” will be offered free and online to students worldwide during the fall of 2011. Students will have access to lecture videos, lecture notes, receive regular feedback on progress, and receive answers to questions. When you successfully complete the class, you will also receive a statement of accomplishment. Taught by Professor Andrew Ng, the curriculum draws from Stanford’s popular Machine Learning course.”

If successful, this might be a game changer. Two weeks after the online AI course was announced, 56,000 students had signed up! The approach might work for many disciplines, not just CS. The Kahn Academy is a related effort.

Universities should keep an eye on them and think about how to adapt if they are successful. Most of our students will probably benefit from taking our traditional courses. If so, we should be able to explain the benefits from taking them (and make sure we deliver those benefits). At the same time, we may want to leverage the online material from these courses in a synergistic way.

The Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one day, free event bringing together faculty, researchers and students from universities in the Mid-Atlantic area working in Speech/Language/ML. The colloquium is an opportunity for students to present preliminary or completed work and to network with other students, faculty and researchers working in related fields. The event will be held in Baltimore MD at the Johns Hopkins University on Friday 23 September 2011.

Students are encouraged to submit one-page abstracts by Monday, August 15 describing ongoing, planned, or completed research projects, including previously published results and negative results. Student research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields, is welcome. Submissions and presentations must be made by students or postdocs. See the call for papers for more information.

Accepted submissions will be presented as posters and each will also be given a one-minute presentation during a poster spotlight session. A small number of submissions will be selected to be presented as talks, on the basis of diversity and general interest.

Student-led breakout sessions of one hour will also be held to discuss papers on topics of interest and stimulate interaction and discussion. Topics and suggested papers for breakout sessions should be submitted by students alongside abstracts.

The event is sponsored by the Human Language Technology Center of Excellence and the Center for Language and Speech Processing at the Johns Hopkins University.

Publishing trends has a good post describing a new variation on spam: creating low-quality ebooks from plagiarized or public-domain content and selling them in ebook markets like Amazon’s Kindle store. If you want to MAKE.MONEY.FAST there are people willing to help:

Automatically detecting these spam ebooks might be a good machine learning project. One problem is that to use features of the ebook itself (e.g., poor formatting) might require purchasing it. But there are sure to be many useful features that the ebook store provides that might support an effective classifier.

(h/t Bruce Schneier)

DARPA is developing a new component to track “quiet submarines” to be part of the Navy’s Anti Submarine Warfare toolkit and is using a software game to collect effective strategies for its use.

“Before autonomous software is developed for ACTUV’s computers, DARPA needs to determine what approaches and methods are most effective. To gather information from a broad spectrum of users, ACTUV has been integrated into the Dangerous Waters™ game. DARPA is offering this new ACTUV Tactics Simulator for free public download.

This software has been written to simulate actual evasion techniques used by submarines, challenging each player to track them successfully. Your tracking vessel is not the only ship at sea, so you’ll need to safely navigate among commercial shipping traffic as you attempt to track the submarine, whose driver has some tricks up his sleeve. You will earn points as you complete mission objectives, and will have the opportunity to see how you rank against the competition on DARPA’s leaderboard page. You can also share your experiences and insights from playing the simulator with others.”

This is a kind of crowdsourcing — leveraging the experiences of a large number of people playing a game. Applying various kinds of machine learning algorithms to the simulator data could be an effective way to train an autonomous tool for this task.

Microsoft Research and Bing are jointly hosting the Speller Challenge. The goal is to build the best service that could propose alternative spellings for search queries submitted to Bing. Entries must be submitted for the challenge in the form of a REST-based web service, and they will be judged based on their expected F1 score against a test set sampled from real Bing queries.

For development purposes, they are making available a TREC evaluation dataset through their Web-NGram service.  Refer to this page for detailed evaluation measures and REST service specs.

Time to start implementing!

IBM’s Watson’s performance in last week’s Jeopardy Challenge was an amazing accomplishment and a demonstration of how our computer systems are becoming more intelligent and capable of solving difficult tasks.

But I wonder if the way that questions were given to the human players and Watson doesn’t give Watson a short, but significant head start. According to the New York Times

“During the sparring matches, Watson received the questions as electronic texts at the same moment they were made visible to the human players;”

Once Watson received a query, it could process it immediately. While the human contestants got to see the query as written text at the same time, Alex Trebek also starts reading the question aloud. When I was watching Jeopardy, I found it almost impossible to read and understand the question more quickly than it was being spoken and suspect that Ken Jennings and Brad Rutter might also. It’s often observed that people find it very difficult to simultaneously process two language streams. While it took Trebek only a second or two to read the short Jeopardy queries, that could have given Watson a significant head start, enabling it to determine that it had a good answer and press its buzzer before the competition.

If this is the case, I am not sure if it is an unfair advantage. People and computers each have native advantages and disadvantages. If Jennings and Rutter got the questions as text without them being simultaneous read aloud, Watson might still have had the advantage of a quicker start.

On the eve of the big Jeopardy! match, Peter Norvig’s opinion piece in the New York Post (!) today, The Machine Age looks at AI’s progress over the past sixty years and lays out six surprising lessons we’ve learned.

• The things we thought were hard turned out to be easier.
• Dealing with uncertainty turned out to be more important than thinking with logical precision.
• Learning turned out to be more important than knowing.
• Current systems are more likely to be built from examples than from logical rules.
• The focus shifted from replacing humans to augmenting them.
• The partnership between human and machine is stronger than either one alone.

When took Pat Winston’s undergraduate AI class in 1970, only the first of those ideas was current. It’s a good essay.

Of course, after we we’ve exploited the new data-driven, statistical paradigm for the next decade or so, we’ll probably have to go back to figuring out how to get logic back into the framework.