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» Classification of microarray data using gene networks
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ISBI
2004
IEEE
16 years 16 days ago
Pareto Depth Sampling Distributions for Gene Ranking
In this paper we propose a method for gene ranking from microarray experiments using multiple discriminants. The novelty of our approach is that a gene's relative rank is det...
Alfred O. Hero, Sepidarseh Zareparsi, Anand Swaroo...
BMCBI
2008
160views more  BMCBI 2008»
14 years 12 months ago
A comparison of four clustering methods for brain expression microarray data
Background: DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they pr...
Alexander L. Richards, Peter Holmans, Michael C. O...
ICPR
2008
IEEE
15 years 6 months ago
A fuzzy c-means algorithm using a correlation metrics and gene ontology
A fuzzy c-means algorithm was adapted for analyzing microarray data. The adaptation consisted of initialization of fuzzy centroids using gene ontology information and the use of P...
Mingrui Zhang, Terry M. Therneau, Michael A. McKen...
BMCBI
2011
14 years 3 months ago
Gene set analysis for longitudinal gene expression data
Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. ...
Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. ...
BIODATAMINING
2008
135views more  BIODATAMINING 2008»
14 years 12 months ago
Fast Gene Ontology based clustering for microarray experiments
Background: Analysis of a microarray experiment often results in a list of hundreds of diseaseassociated genes. In order to suggest common biological processes and functions for t...
Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi