Conference proceeding
Visual exploration of genetic likelihood space
Proceedings of the 2006 ACM symposium on applied computing, Vol.2, pp.1335-1340
SAC '06
04/23/2006
DOI: 10.1145/1141277.1141589
Abstract
Linkage analysis is used to localize human disease genes on the genome and it can involve the exploration and interpretation of a seven-dimensional genetic likelihood space. Existing genetic likelihood exploration techniques are quite cumbersome and slow, and do not help provide insight into the shape and features of the high-dimensional likelihood surface. The objective of our visualization is to provide an efficient visual exploration of the complex genetic likelihood space so that researchers can assimilate more information in the least possible time. In this paper, we present new visualization tools for interactive and efficient exploration of the multi-dimensional likelihood space. Our tools provide interactive manipulation of active ranges of the six model parameters determining the dependent variable, scaled genetic likelihood, or HLOD. Using filtering, color, and an approach inspired by "worlds-within-worlds" [5, 6], researchers can quickly obtain a more informative and insightful visual interpretation of the space.
Details
- Title: Subtitle
- Visual exploration of genetic likelihood space
- Creators
- Juw Park - University of IowaJames Cremer - University of IowaAlberto Segre - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 2006 ACM symposium on applied computing, Vol.2, pp.1335-1340
- Series
- SAC '06
- DOI
- 10.1145/1141277.1141589
- Publisher
- ACM
- Language
- English
- Date published
- 04/23/2006
- Academic Unit
- Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science
- Record Identifier
- 9984259495902771
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