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Article
Open AccessCombining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype u...
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Chapter
Support Vector Machines
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the first section we formulate the learning problem in a statistical framework. A special focus is put on the conc...
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Chapter and Conference Paper
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot eas...
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Article
Open AccessAccurate splice site prediction using support vector machines
For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to...
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Article
Open AccessLearning Interpretable SVMs for Biological Sequence Classification
Support Vector Machines (SVMs) – using a variety of string kernels – have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack int...
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Chapter and Conference Paper
New Methods for Splice Site Recognition
Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Accurate splice site detectors thus form important components of computational gene finders. We...
