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» Learning Methods for DNA Binding in Computational Biology
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RECOMB
2002
Springer
15 years 10 months ago
From promoter sequence to expression: a probabilistic framework
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifie...
Eran Segal, Yoseph Barash, Itamar Simon, Nir Fried...
BMCBI
2007
95views more  BMCBI 2007»
14 years 9 months ago
Methods for estimating human endogenous retrovirus activities from EST databases
Background: Human endogenous retroviruses (HERVs) are surviving traces of ancient retrovirus infections and now reside within the human DNA. Recently HERV expression has been dete...
Merja Oja, Jaakko Peltonen, Jonas Blomberg, Samuel...
BMCBI
2007
98views more  BMCBI 2007»
14 years 9 months ago
Duration learning for analysis of nanopore ionic current blockades
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications ...
Alexander G. Churbanov, Carl Baribault, Stephen Wi...
BMCBI
2010
110views more  BMCBI 2010»
14 years 9 months ago
Discovering local patterns of co - evolution: computational aspects and biological examples
Background: Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn abo...
Tamir Tuller, Yifat Felder, Martin Kupiec
BMCBI
2007
120views more  BMCBI 2007»
14 years 9 months ago
Recognition of interferon-inducible sites, promoters, and enhancers
Background: Computational analysis of gene regulatory regions is important for prediction of functions of many uncharacterized genes. With this in mind, search of the target genes...
Elena A. Ananko, Yury V. Kondrakhin, Tatyana I. Me...