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» Predicting Nucleolar Proteins Using Support-Vector Machines
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BICOB
2009
Springer
14 years 1 days ago
A New Machine Learning Approach for Protein Phosphorylation Site Prediction in Plants
Protein phosphorylation is a crucial regulatory mechanism in various organisms. With recent improvements in mass spectrometry, phosphorylation site data are rapidly accumulating. D...
Jianjiong Gao, Ganesh Kumar Agrawal, Jay J. Thelen...
BMCBI
2008
169views more  BMCBI 2008»
13 years 7 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
ESANN
2006
13 years 8 months ago
Evolino for recurrent support vector machines
Abstract. We introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-...
Jürgen Schmidhuber, Matteo Gagliolo, Daan Wie...
KAIS
2010
144views more  KAIS 2010»
13 years 5 months ago
Boosting support vector machines for imbalanced data sets
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...
Benjamin X. Wang, Nathalie Japkowicz
BMCBI
2010
97views more  BMCBI 2010»
13 years 7 months ago
SeqRate: sequence-based protein folding type classification and rates prediction
Background: Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein des...
Guan Ning Lin, Zheng Wang, Dong Xu, Jianlin Cheng