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» Predicting Nucleolar Proteins Using Support-Vector Machines
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118
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ESANN
2008
15 years 5 months ago
Survival SVM: a practical scalable algorithm
This work advances the Support Vector Machine (SVM) based approach for predictive modelling of failure time data as proposed in [1]. The main results concern a drastic reduction in...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
138
Voted
BMCBI
2010
107views more  BMCBI 2010»
14 years 10 months ago
Interaction prediction and classification of PDZ domains
Background: PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal ...
Sibel Kalyoncu, Ozlem Keskin, Attila Gürsoy
151
Voted
BMCBI
2007
157views more  BMCBI 2007»
15 years 3 months ago
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational
Background: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination wi...
Nico Pfeifer, Andreas Leinenbach, Christian G. Hub...
152
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JBCB
2010
138views more  JBCB 2010»
14 years 10 months ago
Hierarchical Classification of Gene Ontology Terms Using the Gostruct Method
Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used ...
Artem Sokolov, Asa Ben-Hur
186
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KDD
2012
ACM
205views Data Mining» more  KDD 2012»
13 years 6 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich