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» Clustering Genes Using Heterogeneous Data Sources
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RECOMB
2010
Springer
15 years 5 months ago
Algorithms for Detecting Significantly Mutated Pathways in Cancer
Abstract. Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heter...
Fabio Vandin, Eli Upfal, Benjamin J. Raphael
BMCBI
2007
120views more  BMCBI 2007»
15 years 3 months ago
Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarrays
Background: Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls...
Jun Lu, Joseph C. Lee, Marc L. Salit, Margaret C. ...
FAST
2009
15 years 29 days ago
A Framework for Fine-grained Data Integration and Curation, with Provenance, in a Dataspace
Some tasks in a dataspace (a loose collection of heterogeneous data sources) require integration of fine-grained data from diverse sources. This work is often done by end users kn...
David W. Archer, Lois M. L. Delcambre, David Maier
ICML
2004
IEEE
16 years 4 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
BMCBI
2007
136views more  BMCBI 2007»
15 years 3 months ago
Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees
Background: In vertebrates, a large part of gene transcriptional regulation is operated by cisregulatory modules. These modules are believed to be regulating much of the tissue-sp...
Xiaoyu Chen, Mathieu Blanchette