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» On the Complexity of Function Learning
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143
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BMCBI
2006
152views more  BMCBI 2006»
15 years 3 months ago
A two-stage approach for improved prediction of residue contact maps
Background: Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvemen...
Alessandro Vullo, Ian Walsh, Gianluca Pollastri
143
Voted
BMCBI
2007
145views more  BMCBI 2007»
15 years 3 months ago
Improved benchmarks for computational motif discovery
Background: An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods h...
Geir Kjetil Sandve, Osman Abul, Vegard Walseng, Fi...
168
Voted
BMCBI
2007
173views more  BMCBI 2007»
15 years 3 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
142
Voted
BMCBI
2007
138views more  BMCBI 2007»
15 years 3 months ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...
170
Voted
JCB
2008
159views more  JCB 2008»
15 years 3 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther
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