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» Biological Network Inference Using Redundancy Analysis
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JCB
2002
160views more  JCB 2002»
14 years 9 months ago
Inference from Clustering with Application to Gene-Expression Microarrays
There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different u...
Edward R. Dougherty, Junior Barrera, Marcel Brun, ...
BMCBI
2005
212views more  BMCBI 2005»
14 years 9 months ago
PAGE: Parametric Analysis of Gene Set Enrichment
Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes i...
Seon-Young Kim, David J. Volsky
ICML
2008
IEEE
15 years 10 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
INFOCOM
2012
IEEE
13 years 5 days ago
Memory-assisted universal compression of network flows
—Recently, the existence of considerable amount of redundancy in the Internet traffic has stimulated the deployment of several redundancy elimination techniques within the netwo...
Mohsen Sardari, Ahmad Beirami, Faramarz Fekri
BMCBI
2011
14 years 1 months ago
Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis
Background: Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information ca...
Artem Lysenko, Michael Defoin-Platel, Keywan Hassa...