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Full-Text Articles in Bioinformatics
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Dartmouth Scholarship
Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Dartmouth 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.
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
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
Dartmouth 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.