Alexandra Scherbart

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Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the(More)
Within this paper we present the extension of two neural network paradigms for clustering tasks. The Self Organizing feature Maps (SOM) are extended to the Multi SOM approach, and the Neural Gas is extended to aMulti NeuralGas. Some common cluster analysis coefficients (Silhouette Coefficient, Gap Statistics, Calinski-Harabasz Coefficient) have been adapted(More)
In this work, we focus on the problem of training ensembles or, more generally, a set of self-organizing maps (SOMs). In the light of new theory behind ensemble learning, in particular negative correlation learning (NCL), the question arises if SOM ensemble learning can benefit from non-independent learning when the individual learning stages are(More)
Within this paper we present the extension of two neural network paradigms for clustering tasks. The Self Organizing feature Maps (SOM) are extended to the Multi SOM approach, and the Neural Gas is extended to a Multi Neural Gas. Some common cluster analysis coefficients (Silhouette Coefficient, Gap Statistics, Calinski-Harabasz Coefficient) have been(More)
In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type for(More)
Mass spectrometry (MS) is a key technique for the analysis and identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to a better understanding of spectrometry data and improved spectrum evaluation. The goal is to model the relationship between peptides and peptide peak heights in(More)
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