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BMCBI
2008
126views more  BMCBI 2008»
14 years 11 months ago
GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed
Background: The amount of gene expression data in the public repositories, such as NCBI Gene Expression Omnibus (GEO) has grown exponentially, and provides a gold mine for bioinfo...
Rong Chen, Rohan Mallelwar, Ajit Thosar, Shivkumar...
BMCBI
2006
169views more  BMCBI 2006»
14 years 11 months ago
Finding biological process modifications in cancer tissues by mining gene expression correlations
Background: Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This techno...
Giacomo Gamberoni, Sergio Storari, Stefano Volinia
CSB
2005
IEEE
205views Bioinformatics» more  CSB 2005»
15 years 4 months ago
Fractal Clustering for Microarray Data Analysis
DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional sp...
Lu-Yong Wang, Ammaiappan Balasubramanian, Amit Cha...
BMCBI
2004
124views more  BMCBI 2004»
14 years 11 months ago
Tests for finding complex patterns of differential expression in cancers: towards individualized medicine
Background: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g....
James Lyons-Weiler, Satish Patel, Michael J. Becic...
CIBCB
2006
IEEE
15 years 5 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...