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» A Parallel Algorithm for Gene Expressing Data Biclustering
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101
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NIPS
2003
14 years 11 months ago
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms
New feature selection algorithms for linear threshold functions are described which combine backward elimination with an adaptive regularization method. This makes them particular...
Claudio Gentile
73
Voted
BIBE
2007
IEEE
136views Bioinformatics» more  BIBE 2007»
15 years 1 days ago
A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
Yi Zhang, Chris H. Q. Ding, Tao Li
PSB
2004
14 years 11 months ago
Motif Discovery in Heterogeneous Sequence Data
This paper introduces the first integrated algorithm designed to discover novel motifs in heterogeneous sequence data, which is comprised of coregulated genes from a single genome...
Amol Prakash, Mathieu Blanchette, Saurabh Sinha, M...
RECOMB
2008
Springer
15 years 10 months ago
Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles
We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signat...
Igor Ulitsky, Richard M. Karp, Ron Shamir
ICPR
2006
IEEE
15 years 11 months ago
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may ...
Rave Harpaz, Robert M. Haralick