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» Predicting Nucleolar Proteins Using Support-Vector Machines
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ICML
2010
IEEE
13 years 8 months ago
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the m...
Han-Hsing Tu, Hsuan-Tien Lin
IJCNN
2006
IEEE
14 years 1 months ago
Comparing Kernels for Predicting Protein Binding Sites from Amino Acid Sequence
— The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has importan...
Feihong Wu
BMCBI
2010
113views more  BMCBI 2010»
13 years 7 months ago
Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information
Background: Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly b...
Nitish K. Mishra, Gajendra P. S. Raghava
BMCBI
2008
88views more  BMCBI 2008»
13 years 7 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
BMCBI
2010
125views more  BMCBI 2010»
13 years 7 months ago
Large-scale prediction of protein-protein interactions from structures
Background: The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms insid...
Martial Hue, Michael Riffle, Jean-Philippe Vert, W...