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» Predicting Nucleolar Proteins Using Support-Vector Machines
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CIBCB
2007
IEEE
14 years 1 months ago
A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins
Abstract— We applied Support Vector Machines to the prediction of the subcellular localization of transmembrane proteins, and compared the performance of different sequence kerne...
Stefan Maetschke, Marcus Gallagher, Mikael Bod&eac...
ICML
2006
IEEE
14 years 8 months ago
Simpler knowledge-based support vector machines
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we intr...
Quoc V. Le, Alex J. Smola, Thomas Gärtner
HAIS
2009
Springer
13 years 12 months ago
Incremental Kernel Machines for Protein Remote Homology Detection
Abstract. Protein membership prediction is a fundamental task to retrieve information for unknown or unidentified sequences. When support vector machines (SVMs) are associated with...
Lionel Morgado, Carlos Pereira
BMCBI
2011
12 years 11 months ago
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
BMCBI
2006
242views more  BMCBI 2006»
13 years 7 months ago
Predicting residue-wise contact orders in proteins by support vector regression
Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a ne...
Jiangning Song, Kevin Burrage