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ICMLA
2010
13 years 5 months ago
Using Randomised Vectors in Transcription Factor Binding Site Predictions
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may ...
Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alis...
BMCBI
2004
114views more  BMCBI 2004»
13 years 7 months ago
Profiled support vector machines for antisense oligonucleotide efficacy prediction
Background: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises ...
Gustavo Camps-Valls, Alistair M. Chalk, Antonio J....
ICMLA
2008
13 years 9 months ago
Ensemble Machine Methods for DNA Binding
We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription factors (TFs). The goal is to identify both TF target genes and their ...
Yue Fan, Mark A. Kon, Charles DeLisi
ECAI
2004
Springer
14 years 26 days ago
A Generalized Quadratic Loss for Support Vector Machines
The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
Filippo Portera, Alessandro Sperduti
BMCBI
2005
155views more  BMCBI 2005»
13 years 7 months ago
Mining protein function from text using term-based support vector machines
Background: Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We...
Simon B. Rice, Goran Nenadic, Benjamin J. Stapley