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» Using Bayesian networks to analyze expression data
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BMCBI
2006
155views more  BMCBI 2006»
14 years 10 months ago
A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates
Background: Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial co...
Anne-Mette K. Hein, Sylvia Richardson
ICICS
2009
Springer
15 years 4 months ago
Assessing Security Risk to a Network Using a Statistical Model of Attacker Community Competence
We propose a novel approach for statistical risk modeling of network attacks that lets an operator perform risk analysis using a data model and an impact model on top of an attack ...
Tomas Olsson
BIOSYSTEMS
2007
115views more  BIOSYSTEMS 2007»
14 years 10 months ago
Evolving fuzzy rules to model gene expression
This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polis...
Ricardo Linden, Amit Bhaya
IJCAI
2003
14 years 11 months ago
Dynamic Bayesian modeling of the cerebral activity
Conventional methods used for the interpretation of activation data provided by functional neuroimaging techniques provide useful insights on what the networks of cerebral structu...
Vincent Labatut, Josette Pastor, Serge Ruff
RECOMB
2006
Springer
15 years 10 months ago
A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies
Abstract. Pharmacogenomics and clinical studies that measure the temporal expression levels of patients can identify important pathways and biomarkers that are activated during dis...
Naftali Kaminski, Ziv Bar-Joseph