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» Using Bayesian networks to analyze expression data
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NIPS
2003
14 years 11 months ago
Nonstationary Covariance Functions for Gaussian Process Regression
We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smo...
Christopher J. Paciorek, Mark J. Schervish
CSB
2003
IEEE
15 years 3 months ago
LOGOS: a modular Bayesian model for de novo motif detection
The complexity of the global organization and internal structures of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achiev...
Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M...
ICDM
2003
IEEE
158views Data Mining» more  ICDM 2003»
15 years 3 months ago
Identifying Markov Blankets with Decision Tree Induction
The Markov Blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov Blankets inform feature selec...
Lewis Frey, Douglas H. Fisher, Ioannis Tsamardinos...
GISCIENCE
2010
Springer
231views GIS» more  GISCIENCE 2010»
14 years 11 months ago
Efficient Data Collection and Event Boundary Detection in Wireless Sensor Networks Using Tiny Models
Using wireless geosensor networks (WGSN), sensor nodes often monitor a phenomenon that is both continuous in time and space. However, sensor nodes take discrete samples, and an ana...
Kraig King, Silvia Nittel
BMCBI
2004
124views more  BMCBI 2004»
14 years 9 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...