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» Maximum entropy methods for biological sequence modeling
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BMCBI
2007
105views more  BMCBI 2007»
13 years 9 months ago
Generating confidence intervals on biological networks
Background: In the analysis of networks we frequently require the statistical significance of some network statistic, such as measures of similarity for the properties of interact...
Thomas Thorne, Michael P. H. Stumpf
ALENEX
2003
137views Algorithms» more  ALENEX 2003»
13 years 10 months ago
The Markov Chain Simulation Method for Generating Connected Power Law Random Graphs
Graph models for real-world complex networks such as the Internet, the WWW and biological networks are necessary for analytic and simulation-based studies of network protocols, al...
Christos Gkantsidis, Milena Mihail, Ellen W. Zegur...
PR
2006
89views more  PR 2006»
13 years 9 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
BMCBI
2005
141views more  BMCBI 2005»
13 years 9 months ago
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models
Background: G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligan...
Nikolaos G. Sgourakis, Pantelis G. Bagos, Panagiot...
CONSTRAINTS
2008
95views more  CONSTRAINTS 2008»
13 years 9 months ago
Efficient Sequence Alignment with Side-Constraints by Cluster Tree Elimination
Aligning DNA and protein sequences is a core technique in molecular biology. Often, it is desirable to include partial prior knowledge and conditions in an alignment. Going beyond...
Sebastian Will, Anke Busch, Rolf Backofen