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Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.
Ratio-based decisions and the quantitative analysis of cDNA microarray images.
Gene expression can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA micro-array and based on a hypothesis test and confidence interval to quantify the significance of observed differences in expression ratios.
Hands-on Morphological Image Processing
Morphological image processing, a standard part of the imaging scientist's toolbox, can be applied to a wide range of industrial applications and shows how to analyse the problems and then develop successful algorithms to solve them.
Fuzzification of set inclusion: theory and applications
Gene selection: a Bayesian variable selection approach
A hierarchical Bayesian model for gene (variable) selection is proposed and applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCa2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes.
From Boolean to probabilistic Boolean networks as models of genetic regulatory networks
The central theme in this paper is the Boolean formalism as a building block for modeling complex, large-scale, and dynamical networks of genetic interactions and its relationships to nonlinear digital filters.
Morphological methods in image and signal processing
Is cross-validation valid for small-sample microarray classification?
An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data.