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» Informative sampling for large unbalanced data sets
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BMCBI
2008
115views more  BMCBI 2008»
13 years 7 months ago
Improving peptide-MHC class I binding prediction for unbalanced datasets
Background: Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such bin...
Ana Paula Sales, Georgia D. Tomaras, Thomas B. Kep...
GECCO
2006
Springer
186views Optimization» more  GECCO 2006»
13 years 11 months ago
Characterizing large text corpora using a maximum variation sampling genetic algorithm
An enormous amount of information available via the Internet exists. Much of this data is in the form of text-based documents. These documents cover a variety of topics that are v...
Robert M. Patton, Thomas E. Potok
BMCBI
2008
119views more  BMCBI 2008»
13 years 7 months ago
A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics
Background: In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differe...
Sabine Pérès, Laurence Molina, Nicol...
VC
2008
88views more  VC 2008»
13 years 7 months ago
Evolution of T-spline level sets for meshing non-uniformly sampled and incomplete data
Given a large set of unorganized point sample data, we propose a new framework for computing a triangular mesh representing an approximating piecewise smooth surface. The data may ...
Huaiping Yang, Bert Jüttler
PKDD
2001
Springer
104views Data Mining» more  PKDD 2001»
14 years 1 days ago
Data Reduction Using Multiple Models Integration
Large amount of available information does not necessarily imply that induction algorithms must use all this information. Samples often provide the same accuracy with less computat...
Aleksandar Lazarevic, Zoran Obradovic