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» Learning Methods for DNA Binding in Computational Biology
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RECOMB
2006
Springer
14 years 7 months ago
Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence
Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consi...
Sauro Menchetti, Andrea Passerini, Paolo Frasconi,...
BMCBI
2010
121views more  BMCBI 2010»
13 years 4 months ago
Computing DNA duplex instability profiles efficiently with a two-state model: trends of promoters and binding sites
Background: DNA instability profiles have been used recently for predicting the transcriptional start site and the location of core promoters, and to gain insight into promoter ac...
Miriam R. Kantorovitz, Zoi Rapti, Vladimir Gelev, ...
RECOMB
2005
Springer
14 years 7 months ago
Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions
Many important cellular response mechanisms are activated when a peptide binds to an appropriate receptor. In the immune system, the recognition of pathogen peptides begins when th...
Chen Yanover, Tomer Hertz
RECOMB
2001
Springer
14 years 7 months ago
Context-specific Bayesian clustering for gene expression data
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. I...
Yoseph Barash, Nir Friedman
BMCBI
2007
136views more  BMCBI 2007»
13 years 7 months ago
Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees
Background: In vertebrates, a large part of gene transcriptional regulation is operated by cisregulatory modules. These modules are believed to be regulating much of the tissue-sp...
Xiaoyu Chen, Mathieu Blanchette