E. Dougherty

Publications
Influence

Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

I. Shmulevich, E. Dougherty, S. Kim, W. Zhang
Computer Science, Medicine
Bioinform.
1 February 2002

We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. Expand

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Ratio-based decisions and the quantitative analysis of cDNA microarray images.

Y. Chen, E. Dougherty, M. Bittner
Medicine
Journal of biomedical optics
1 October 1997

Gene expression can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA micro-array. Comparison of gene expression levels arising from co-hybridized samples is achieved… Expand

Fuzzification of set inclusion: theory and applications

D. Sinha, E. Dougherty
Mathematics
9 April 1993

Abstract Fuzzification of set inclusion for fuzzy sets is developed in terms of an indicator for set inclusion, the indicator giving the degree to which a fuzzy set is a subset of another fuzzy set.… Expand

Hands-on Morphological Image Processing

E. Dougherty, R. Lotufo
Computer Science
24 July 2003

Morphological image processing, a standard part of the imaging scientist's toolbox, can be applied to a wide range of industrial applications. Expand

From Boolean to probabilistic Boolean networks as models of genetic regulatory networks

I. Shmulevich, E. Dougherty, W. Zhang
Computer Science
10 December 2002

This paper introduces the Boolean formalism as a building block for modeling complex, large-scale, and dynamical networks of genetic interactions. Expand

Is cross-validation valid for small-sample microarray classification?

U. Braga-Neto, E. Dougherty
Computer Science, Medicine
Bioinform.
12 February 2004

We compare cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees-using both synthetic and real breast-cancer patient data. Expand

An introduction to morphological image processing

E. Dougherty
Computer Science
1992

Gene selection: a Bayesian variable selection approach

K. Lee, N. Sha, E. Dougherty, M. Vannucci, B. Mallick
Biology, Computer Science
Bioinform.
2003

We propose a hierarchical Bayesian model for gene (variable) selection using latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. Expand

Coefficient of determination in nonlinear signal processing

E. Dougherty, S. Kim, Y. Chen
Mathematics, Computer Science
Signal Process.
1 October 2000

This paper discusses the coe$cient of determination in the context of nonlinear digital signal and image processing. Expand

Morphological methods in image and signal processing

C. Giardina, E. Dougherty
Computer Science
1988

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