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BioInform s Funding Update: NSF Bioinformatics Awards to Oct. 30, 2004

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Support Vector Methods for Functional Genomic Analysis. Start date: April 2004. Expires: March 2007. Amount: $158,517. Principal investigator: William Noble. Sponsor: University of Washington.

Project will develop machine-learning techniques that will classify genes into discrete functional categories in order to infer gene function from genomic data. The grantee will build on prior work, which showed that a support vector machine can be successfully trained using DNA microarray expression data to recognize various gene functional categories.


Numerical Methods for Molecular Dynamics. Start date: August 2004. Expires: June 2005. Amount: $47,982. Principal investigator: Robert Skeel. Sponsor: Purdue University.

Funds development of new algorithms for molecular dynamics using techniques such as modified energy functions that compensate for finite steps, stochastic stabilization, and optimization of method parameters. The algorithms will be distributed for public use in a project at the University of Illinois Beckman Institute.


Building Biologically Based Immune System Simulations for Education and Training. Start date: October 2004. Expires: August 2007. This grant is awarded to two investigative teams:

  • Brown University. Principal investigator: Andries van Dam. Amount: $160,000.
  • Federation of American Scientists. Principal Investigator: Henry Kelly. Amount: $855,319.

Project to develop a computer simulation of the human immune system for use by biology students at the high school and freshman college levels.


Knowledge Extraction from Support Vector Machines Models of Nonlinear Drug Data Sets. Start date: January 2005. Expires: June 2005. Amount: $99,434. Principal investigator: Hamilton Hitchings. Sponsor: Equbits.

Phase I SBIR project to investigate the feasibility of knowledge extraction from support vector machine nonlinear models and their results for drug data sets. According to the grantee, “the lack of interpretability of nonlinear models has prevented successful commercial adoption of SVMs by the pharmaceutical industry.”

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