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HCI
2007
13 years 10 months ago
FPF-SB : A Scalable Algorithm for Microarray Gene Expression Data Clustering
Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the ...
Filippo Geraci, Mauro Leoncini, Manuela Montangero...
BMCBI
2010
118views more  BMCBI 2010»
13 years 9 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...
SAC
2006
ACM
14 years 2 months ago
Two-phase clustering strategy for gene expression data sets
In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and con...
Dirk Habich, Thomas Wächter, Wolfgang Lehner,...
AUSAI
2005
Springer
14 years 2 months ago
Finding Similar Patterns in Microarray Data
Abstract. In this paper we propose a clustering algorithm called sCluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight cluster...
Xiangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi...
BIOINFORMATICS
2010
250views more  BIOINFORMATICS 2010»
13 years 7 months ago
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here we present DEGseq, an R package to identify di...
Likun Wang, Zhixing Feng, Xi Wang, Xiaowo Wang, Xu...