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2010 Neukom Scholars

Winners of Neukom Scholarships for the 2009-2010 academic year were: 

 R & D to Create Interactive real time data visualization pulling from gps and sensor-gathered environmental data

Darren Cheng
John Eikens
Chris Bailey-Kellogg - Faculty Advisor (Computer Science)

Movements of wild Tanzanian Wildebeest (as seen through gps tracking data streams) are channeled into our environment carrying with it information on the speed, direction, gender, age and location of each sampled animal. When human visitors enter the project space, they will also be charted, their movements, rhythms, velocities, shapes and directions re-processed into a continuous stream.

 The wildebeest and human patterns will influence each other, play off each other, change each other, create something new. Wildebeat Project

Proposal for Computing a Table of Generalized Knots

Daniel Denton
Peter Doyle - Faculty Advisor (Mathematics)

Virtual knots have been an active area of research in knot theory since they were proposed by Louis Kaufman in 1999. In brief, virtual knots occur when a set of specified crossing cannot be realized as a classical knot diagram on the plane because they form a non-planar graph. I propose to calculate a table of generalized knot equivalence classes for some of the smaller knots.

Publication:

Shadow movies not arising from knots (pdf) 

 

Pathway Studio Helps Enhance the Power of MDR in Identifying Cancer Genes

Arvis Sulovari
Jason Moore - Faculty Advisor (Genetics)

The causative factors of cancer are known to be very diverse and challenging to identify. However, studies across different fields support the hypothesis that Single Nucleotide Polymorphisms (SNPs) play a major causative role in cancer. SNP lists are constructed for different types of cancer. These lists contain polymorphic genes, which are known to be either directly related to a particular type of cancer, or are part of a pathway that eventually leads to cancer. A bladder cancer SNP dataset, containing 1445 SNPs, will be provided from previous studies done at Moore Lab. The 1445 SNP will be analyzed by the Pathway Studio software platform. A python script will be written to simulate data according to a model with different heritability values. The final step of this analysis will involve using the Multifactor Dimensionality Reduction (MDR) software, which has been developed in the Moore Lab. MDR allows for incorporation of expert knowledge as heuristic information in the analysis of SNP datasets.

Last Updated: 9/15/14