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Project

Kernel Based Methods for Microarray and Mass Spectrometry Data Analysis (Kernel gebaseerde methoden voor gegevensanalyse van microroosters en massaspectrometrie)

Kernel learning methods are advanced and powerful techniques that  allow the construction of non-linear models for classification and  regression problems.
Microarray and mass spectrometry data sources measure the activity and/or expression of thousands of genes and proteins, respectively, on a given set of biological samples. Analysis of the information contained in such samples has become a crucial activity in cancer research during the last decade. However, common problems encountered on these biological data sources are related to: large number of variables compared to the number of examples, low signal to noise ratio, irrelevant variables and the presence of missing values and
outliers. Additionally, current methodologies are not totally well established and results are not always reproducible.
The goal of the proposed research is mainly the application of existing kernel-based methods and their subsequent adaptation to the areas of microarray and mass spectrometry data analysis. Topics included are among others preprocessing, prediction/classification models, variable selection (gene selection or biomarker identification), novelty detection. Model selection will play a central role in the construction of reliable and reproducible algorithms.
Datum:14 apr 2008 →  20 mei 2011
Trefwoorden:Algorithms
Disciplines:Controlesystemen, robotica en automatisatie, Ontwerptheorieën en -methoden, Mechatronica en robotica, Computertheorie, Modellering, Biologische systeemtechnologie, Signaalverwerking, Toegepaste wiskunde, Computerarchitectuur en -netwerken, Distributed computing, Informatiewetenschappen, Informatiesystemen, Programmeertalen, Scientific computing, Theoretische informatica, Visual computing, Andere informatie- en computerwetenschappen
Project type:PhD project