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[colloquium] PICASso Talk TODAY "Aggregating Human Expertise: An Application for Alternating Projection Algorithms"
by Steven Kleinstein 17 Oct '05

17 Oct '05

Thought you might be interested in today's PICASso seminar on "Aggregating Human Expertise: An Application for Alternating Projection Algorithms". An announcement is below. Cheers, -Steve... ** PICASso: ** Program in Integrative Information, Computer and Application Sciences ** www.cs.princeton.edu/picasso ** Monday, October 17, 2005 ** ** Interdisciplinary Computational Lunchtime Seminars ** www.cs.princeton.edu/picasso?computational_lunch.html TITLE: Aggregating Human Expertise: An Application for Alternating Projection Algorithms SPEAKER: Joel B. Predd, Department of Electrical Engineering, Princeton University TIME: Seminar begins 12:30 pm (lunch available at 12:20) LOCATION: Room 302, 3rd Floor, Computer Science Building ABSTRACT: In the panel aggregation problem, each expert on a panel forecasts the chance of a set of events (about, say, the future state of the stock market). The events in question are logically complex and dependent; since the judges are human, the forecasts are plagued by probabilistic incoherence. Several methods have been proposed to fuse the experts' disparate forecasts into a coherent corpus. For problems of interest, these methods are impractical in theory and practice; applications in risk assessment, marketing, and business demand a new approach. What is an efficient algorithm for aggregating the advice of hundreds of judges who provide forecasts for thousands of events? In this talk, we discuss ongoing research aimed at addressing this question using alternating projection algorithms (e.g., the von Neumann-Halperin algorithm). In particular, we show how such classical algorithms can be applied to construct fast and scalable algorithms for aggregating forecasts of chance. We validate the algorithms with experiments in market and geopolitical forecasting. In addition, we discuss how the same methods are useful in other distributed decision making tasks. In particular, we discuss how von Neumann-Halperin can be applied to derive energy and bandwidth efficient training algorithms for distributed kernel regression in wireless sensor networks. Joint work with Professors Sanjeev Kulkarni (EE), Daniel Osherson (Psych), and Vincent Poor (EE). **************** If you would like to be kept informed of computationally-oriented events in (and around) Princeton, please SUBSCRIBE to the PICASso mailing list by visiting https://lists.cs.princeton.edu/mailman/listinfo/picasso. This page also contains information on how to UNSUBSCRIBE. PLEASE FORWARD THIS MESSAGE TO OTHER COMPUTATIONALLY-ORIENTED RESEARCHERS WHO MAY BE INTERESTED IN THESE EVENTS, OR FUTURE PROGRAMS. THANKS!

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[colloquium] PICASso Talk Reminder: Prediction of protein function viagraph-theoretic analysis of interaction maps
by Sandy Barbu 12 Oct '05

12 Oct '05

TALK ANNOUNCEMENT: ------------------ TITLE: Prediction of protein function via graph-theoretic analysis of interaction maps SPEAKER: Elena Nabieva Department of Computer Science TIME: Wednesday, October 12, 2005 LOCATION: Room 402, Computer Science Building ABSTRACT: Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Traditionally, computational methods to assign protein function have relied largely on sequence homology. Recently, the emergence of high-throughput experimental data sets led to a number of alternative, non-homology based methods for functional annotations. In particular, recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. We have developed a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks. SIGN UP FOR THE PICASSO MAILING LIST: ===================================== If you would like to be kept informed of computationally-oriented events in (and around) Princeton, please SUBSCRIBE to the PICASso mailing list by visiting https://lists.cs.princeton.edu/mailman/listinfo/picasso. This page also contains information on how to UNSUBSCRIBE. PLEASE FORWARD THIS MESSAGE TO OTHER COMPUTATIONALLY-ORIENTED RESEARCHERS WHO MAY BE INTERESTED IN THESE EVENTS, OR FUTURE PROGRAMS. THANKS! _______________________________________________ PICASso mailing list PICASso(a)lists.cs.princeton.edu https://lists.cs.princeton.edu/mailman/listinfo/picasso _______________________________________________ announce mailing list announce(a)lists.cs.princeton.edu https://lists.cs.princeton.edu/mailman/listinfo/announce _______________________________________________ PICASso mailing list PICASso(a)lists.cs.princeton.edu https://lists.cs.princeton.edu/mailman/listinfo/picasso

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[colloquium] Helen Berman is giving the CS colloquium on Wednesday
by Thomas A. Funkhouser 10 Oct '05

10 Oct '05

Helen Berman, director of the Protein Data Bank, will be visiting Princeton on Wednesday and giving a talk in the CS colloquium at 4PM in the small auditorium (Room 105). #################################################### The Protein Data Bank: Structure and Function Helen Berman The Protein Data Bank is the international repository for the structures of all biological macromolecules. The ways in which the data are represented, collected, archived, and queried will be described. The ways in which the PDB resource has been designed so as to accommodate a very diverse user community will be discussed.

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[colloquium] TUTORIAL TODAY: Experimental Design and Statistical Analysis for Microarray Experiments
by Steven Kleinstein 07 Oct '05

07 Oct '05

** PICASso: ** Program in Integrative Information, Computer and Application Sciences ** www.cs.princeton.edu/picasso ** Experimental Design and Statistical Analysis ** for Microarray Experiments ** October 7 (1:30 - 5:00 PM) ** Small Auditorium, Computer Science Building (room 105) ** http://www.cs.princeton.edu/picasso?events.html PICASso is pleased to announce an introductory tutorial on "Experimental Design and Statistical Analysis for Microarray Experiments" taught by Thomas J. Downey (President, Partek Inc). Microarray data contains treatment and/or phenotype effects embedded in a sea of technical and biological noise. This mini-course will demonstrate how to use proven statistical methods of experiment design and analysis of variance (ANOVA) to reliably identify biological effects of interest while controlling and removing noise due to biological and technical nuisance effects. Attendees will learn what ANOVA means, what it does, and how it can be used to isolate and remove unwanted technical and biological noise, clearly revealing the signals from the biological factors of interest. In addition, estimates of ratios and fold-changes will be examined from a statistical perspective. The impact of the assumptions of normality, equal variance, and independence will also be addressed for small sample sizes and for large studies as well. Finally, a variety of statistical visualizations will be used to interpret the results of the analysis. This tutorial is designed for researchers performing microarray, or any genomic, or biological studies, but also useful for those wishing to learn more about designing and analyzing experiments in a way that results in substantial noise reduction. It will be held on Princeton's main campus today, October 7th (1:30 - 5:00 PM), Computer Science Building, small auditorium (room 105). All are welcome! Registration is not required.

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