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Life Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

2006

Genetics and Genomics

Algorithms

Articles 1 - 3 of 3

Full-Text Articles in Life Sciences

Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross May 2006

Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross

Open Dartmouth: Faculty Open Access Scholarship

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.


Dissecting Trait Heterogeneity: A Comparison Of Three Clustering Methods Applied To Genotypic Data, Tricia A. Thornton-Wells, Jason H. Moore, Jonathan L. Haines Apr 2006

Dissecting Trait Heterogeneity: A Comparison Of Three Clustering Methods Applied To Genotypic Data, Tricia A. Thornton-Wells, Jason H. Moore, Jonathan L. Haines

Open Dartmouth: Faculty Open Access Scholarship

Background: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex hu man disease. In the absence of de tailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heteroge neity. The performance of three such methods – Bayesian Classification, Hyperg raph-Based Clustering, and Fuzzy k -Modes Clustering – appropriate for categorical data were comp ared. Also tested was the ability of these methods to detect ...


Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie Jan 2006

Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie

Open Dartmouth: Faculty Open Access Scholarship

The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.