Matches in SemOpenAlex for { <https://semopenalex.org/work/W2116728532> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W2116728532 endingPage "1571" @default.
- W2116728532 startingPage "1565" @default.
- W2116728532 abstract "Summary: Differential gene expression detection using microarrays has received lots of research interests recently. Many methods have been proposed, including variants of F-statistics, non-parametric approaches and empirical Bayesian methods etc. The SAM statistics has been shown to have good performance in empirical studies. SAM is more like an ad hoc shrinkage method. The idea is that for small sample microarray data, it is often useful to pool information across genes to improve efficiency. Under Bayesian framework Smyth formally derived the test statistics with shrinkage using the hierarchical models. In this paper we cast differential gene expression detection in the familiar framework of linear regression model. Commonly used test statistics correspond to using least squares to estimate the regression parameters. Based on the vast literature of research on linear models, we can naturally consider other alternatives. Here we explore the penalized linear regression. We propose the penalized t-/F-statistics for two-class microarray data based on ({mathcal{L}}_{1}) penalty. We will show that the penalized test statistics intuitively makes sense and through applications we illustrate its good performance. Availability: Supplementary information including program codes, more detailed analysis results and R functions for the proposed methods can be found at http://www.biostat.umn.edu/~baolin/research Contact: baolin@biostat.umn.edu Supplementary information: http://www.biostat.umn.edu/~baolin/research" @default.
- W2116728532 created "2016-06-24" @default.
- W2116728532 creator A5029703168 @default.
- W2116728532 date "2004-12-14" @default.
- W2116728532 modified "2023-10-14" @default.
- W2116728532 title "Differential gene expression detection using penalized linear regression models: the improved SAM statistics" @default.
- W2116728532 cites W1495359568 @default.
- W2116728532 cites W2063978378 @default.
- W2116728532 cites W2074089196 @default.
- W2116728532 cites W2100668965 @default.
- W2116728532 cites W2104695131 @default.
- W2116728532 cites W2109363337 @default.
- W2116728532 cites W2116811581 @default.
- W2116728532 cites W2120865735 @default.
- W2116728532 cites W238668910 @default.
- W2116728532 cites W4229880569 @default.
- W2116728532 cites W4247420482 @default.
- W2116728532 cites W4294107304 @default.
- W2116728532 doi "https://doi.org/10.1093/bioinformatics/bti217" @default.
- W2116728532 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15598833" @default.
- W2116728532 hasPublicationYear "2004" @default.
- W2116728532 type Work @default.
- W2116728532 sameAs 2116728532 @default.
- W2116728532 citedByCount "48" @default.
- W2116728532 countsByYear W21167285322012 @default.
- W2116728532 countsByYear W21167285322013 @default.
- W2116728532 countsByYear W21167285322014 @default.
- W2116728532 countsByYear W21167285322016 @default.
- W2116728532 countsByYear W21167285322017 @default.
- W2116728532 countsByYear W21167285322018 @default.
- W2116728532 countsByYear W21167285322019 @default.
- W2116728532 crossrefType "journal-article" @default.
- W2116728532 hasAuthorship W2116728532A5029703168 @default.
- W2116728532 hasBestOaLocation W21167285321 @default.
- W2116728532 hasConcept C105795698 @default.
- W2116728532 hasConcept C107673813 @default.
- W2116728532 hasConcept C124101348 @default.
- W2116728532 hasConcept C129848803 @default.
- W2116728532 hasConcept C33923547 @default.
- W2116728532 hasConcept C41008148 @default.
- W2116728532 hasConcept C48921125 @default.
- W2116728532 hasConcept C83546350 @default.
- W2116728532 hasConcept C87007009 @default.
- W2116728532 hasConceptScore W2116728532C105795698 @default.
- W2116728532 hasConceptScore W2116728532C107673813 @default.
- W2116728532 hasConceptScore W2116728532C124101348 @default.
- W2116728532 hasConceptScore W2116728532C129848803 @default.
- W2116728532 hasConceptScore W2116728532C33923547 @default.
- W2116728532 hasConceptScore W2116728532C41008148 @default.
- W2116728532 hasConceptScore W2116728532C48921125 @default.
- W2116728532 hasConceptScore W2116728532C83546350 @default.
- W2116728532 hasConceptScore W2116728532C87007009 @default.
- W2116728532 hasIssue "8" @default.
- W2116728532 hasLocation W21167285321 @default.
- W2116728532 hasLocation W21167285322 @default.
- W2116728532 hasOpenAccess W2116728532 @default.
- W2116728532 hasPrimaryLocation W21167285321 @default.
- W2116728532 hasRelatedWork W1986523067 @default.
- W2116728532 hasRelatedWork W2067177470 @default.
- W2116728532 hasRelatedWork W2288557197 @default.
- W2116728532 hasRelatedWork W2496077116 @default.
- W2116728532 hasRelatedWork W3082212156 @default.
- W2116728532 hasRelatedWork W31220157 @default.
- W2116728532 hasRelatedWork W3152660226 @default.
- W2116728532 hasRelatedWork W4243140484 @default.
- W2116728532 hasRelatedWork W4293088233 @default.
- W2116728532 hasRelatedWork W4386762140 @default.
- W2116728532 hasVolume "21" @default.
- W2116728532 isParatext "false" @default.
- W2116728532 isRetracted "false" @default.
- W2116728532 magId "2116728532" @default.
- W2116728532 workType "article" @default.