409 search results - page 54 / 82 » Predicting Nucleolar Proteins Using Support-Vector Machines |
146
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ICASSP
2011
IEEE
14 years 7 months ago
2011
IEEE
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
139
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ECML
2006
Springer
15 years 7 months ago
2006
Springer
Abstract We propose in this paper a novel approach to the classification of discrete sequences. This approach builds a model fitting some dynamical features deduced from the learni...
140
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GECCO
2008
Springer
15 years 4 months ago
2008
Springer
A genetic algorithm (GA) is utilised to discover known and novel PROSITE-like sequence templates that can be used to classify the sub-cellular location of eukaryotic proteins. Whi...
125
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BMCBI
2008
15 years 3 months ago
2008
Background: Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on differen...
159
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CIBCB
2009
IEEE
15 years 4 months ago
2009
IEEE
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as activ...