Detecting disease-causing genes by LASSO-Patternsearch algorithm
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چکیده
منابع مشابه
Detecting disease-causing genes by LASSO-Patternsearch algorithm
The Genetic Analysis Workshop 15 Problem 3 simulated rheumatoid arthritis data set provided 100 replicates of simulated single-nucleotide polymorphism (SNP) and covariate data sets for 1500 families with an affected sib pair and 2000 controls, modeled after real rheumatoid arthritis data. The data generation model included nine unobserved trait loci, most of which have one or more of the genera...
متن کاملLASSO-Patternsearch algorithm with application to ophthalmology and genomic data.
The LASSO-Patternsearch algorithm is proposed to efficiently identify patterns of multiple dichotomous risk factors for outcomes of interest in demographic and genomic studies. The patterns considered are those that arise naturally from the log linear expansion of the multivariate Bernoulli density. The method is designed for the case where there is a possibly very large number of candidate pat...
متن کاملLASSO-Patternsearch Algorithm with Application to Ophthalmology Data
The LASSO-Patternsearch is proposed, as a two-stage procedure to identify clusters of multiple risk factors for outcomes of interest in large demographic studies, when the predictor variables are dichotomous or take on values in a small finite set. Many diseases are suspected of having multiple interacting risk factors acting in concert, and it is of much interest to uncover higher order intera...
متن کاملCredibility Analysis of Putative Disease-Causing Genes Using Bioinformatics
BACKGROUND Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective...
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ژورنال
عنوان ژورنال: BMC Proceedings
سال: 2007
ISSN: 1753-6561
DOI: 10.1186/1753-6561-1-s1-s60