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» Prediction of glycosylation sites using random forests
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ESANN
2006
13 years 9 months ago
Using sampling methods to improve binding site predictions
Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling with th...
Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhors...
BIBE
2007
IEEE
167views Bioinformatics» more  BIBE 2007»
13 years 11 months ago
Assessing the Performance of Macromolecular Sequence Classifiers
Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational bio...
Cornelia Caragea, Jivko Sinapov, Vasant Honavar, D...
ICML
2010
IEEE
13 years 8 months ago
Supervised Aggregation of Classifiers using Artificial Prediction Markets
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. In this work we introduce a mathematical theory for Artificial Prediction ...
Nathan Lay, Adrian Barbu
ECRIME
2007
13 years 11 months ago
A comparison of machine learning techniques for phishing detection
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting ph...
Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Na...
BMCBI
2008
134views more  BMCBI 2008»
13 years 7 months ago
Prediction of protein-protein binding site by using core interface residue and support vector machine
Background: The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for th...
Nan Li, Zhonghua Sun, Fan Jiang