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BMCBI
2010
118views more  BMCBI 2010»
13 years 7 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
BMCBI
2011
13 years 2 months ago
Worm Phenotype Ontology: integrating phenotype data within and beyond the C. elegans community
Background: Caenorhabditis elegans gene-based phenotype information dates back to the 1970’s, beginning with Sydney Brenner and the characterization of behavioral and morphologi...
Gary Schindelman, Jolene Fernandes, Carol Bastiani...
BMCBI
2007
194views more  BMCBI 2007»
13 years 7 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
BMCBI
2008
132views more  BMCBI 2008»
13 years 7 months ago
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of
Background: Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analys...
Raffaele Giancarlo, Davide Scaturro, Filippo Utro
BMCBI
2008
112views more  BMCBI 2008»
13 years 7 months ago
A general modeling and visualization tool for comparing different members of a group: application to studying tau-mediated regul
Background: Innumerable biological investigations require comparing collections of molecules, cells or organisms to one another with respect to one or more of their properties. Al...
Arnab Bhattacharya, Sasha Levy, Adria LeBoeuf, Mic...