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» Learning Methods for DNA Binding in Computational Biology
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JMLR
2006
156views more  JMLR 2006»
14 years 9 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
BMCBI
2008
164views more  BMCBI 2008»
14 years 9 months ago
Word correlation matrices for protein sequence analysis and remote homology detection
Background: Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provid...
Thomas Lingner, Peter Meinicke
RECOMB
2007
Springer
15 years 10 months ago
A Bayesian Model That Links Microarray mRNA Measurements to Mass Spectrometry Protein Measurements
Abstract. An important problem in biology is to understand correspondences between mRNA microarray levels and mass spectrometry peptide counts. Recently, a compendium of mRNA expre...
Anitha Kannan, Andrew Emili, Brendan J. Frey
BMCBI
2005
103views more  BMCBI 2005»
14 years 9 months ago
Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery
Background: Molecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such...
Leslie Grate
84
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RECOMB
2007
Springer
15 years 10 months ago
Support Vector Training of Protein Alignment Models
Abstract. Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features like secondary structure, solvent accessibility...
Chun-Nam John Yu, Thorsten Joachims, Ron Elber, Ja...