Abstract
For systematic analyses of quantitative mass spectrometry data a method was developed in order to reveal peptides within a protein, that show differences in comparison with the remaining peptides of the protein concerning their regulatory characteristics. Regulatory information is calculated and visualised by a probabilistic approach resulting in likelihood curves. On the other hand the algorithm for the detection of one or more clusters is based on fuzzy clustering, so that our hybrid approach combines probabilistic concepts as well as principles from soft computing. The test is able to decide whether peptides belonging to the same protein, cluster into one or more group. In this way obtained information is very valuable for the detection of single peptides or peptide groups which can be regarded as regulatory outliers.
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Hundertmark, C., Klawonn, F. (2008). Clustering Likelihood Curves: Finding Deviations from Single Clusters. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_48
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DOI: https://doi.org/10.1007/978-3-540-87656-4_48
Publisher Name: Springer, Berlin, Heidelberg
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