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» A unified approach to false discovery rate estimation
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TMI
2010
172views more  TMI 2010»
14 years 7 months ago
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
Abstract— We compared four automated methods for hippocampal segmentation using different machine learning algorithms (1) hierarchical AdaBoost, (2) Support Vector Machines (SVM)...
Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova...
CSDA
2007
151views more  CSDA 2007»
14 years 9 months ago
Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments
An important goal of microarray studies is the detection of genes that show significant changes in observed expressions when two or more classes of biological samples such as tre...
Marco Alfò, Alessio Farcomeni, Luca Tardell...
BMCBI
2006
156views more  BMCBI 2006»
14 years 9 months ago
Bayesian models for pooling microarray studies with multiple sources of replications
Background: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides with...
Erin M. Conlon, Joon J. Song, Jun S. Liu
BMCBI
2010
146views more  BMCBI 2010»
14 years 9 months ago
On the Choice and Number of Microarrays for Transcriptional Regulatory Network Inference
Background: Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)...
Elissa J. Cosgrove, Timothy S. Gardner, Eric D. Ko...
BMCBI
2008
122views more  BMCBI 2008»
14 years 9 months ago
Reconstructing networks of pathways via significance analysis of their intersections
Background: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statist...
Mirko Francesconi, Daniel Remondini, Nicola Nerett...