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» Classification of Random Boolean Networks
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ICML
2003
IEEE
14 years 10 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
BMCBI
2008
173views more  BMCBI 2008»
13 years 10 months ago
Extraction of semantic biomedical relations from text using conditional random fields
Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of a...
Markus Bundschus, Mathäus Dejori, Martin Stet...
IJOE
2006
75views more  IJOE 2006»
13 years 9 months ago
Comparison of Intensive and Extensive Sensor Networking Technologies
The objective of this paper is to emphasize a clear and natural distinction in strategies of sensor network design. In order to display different architectural paradigms in today&#...
Marek Miskowicz
GECCO
2004
Springer
132views Optimization» more  GECCO 2004»
14 years 3 months ago
Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments
Abstract. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. Different approaches to infer the dependencies of gene r...
Christian Spieth, Felix Streichert, Nora Speer, An...
JCB
2006
114views more  JCB 2006»
13 years 9 months ago
A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics
A stochastic genetic toggle switch model that consists of two identical, mutually repressive genes is built using the Gillespie algorithm with time delays as an example of a simpl...
Andre Ribeiro, Rui Zhu, Stuart A. Kauffman