Abstract
During the last years, various methodologies have made possible the detection of large parts of the protein interaction network of various organisms. However, these networks are containing highly noisy data, degrading the quality of information they carry. Various weighting schemes have been applied in order to eliminate noise from interaction data and help bioinformaticians to extract valuable information such as the detection of protein complexes. In this contribution, we propose the addition of an extra step on these weighting schemes by using kernel methods to better assess the reliability of each pairwise interaction. Our experimental results prove that kernel methods clearly help the elimination of noise by producing improved results on the protein complexes detection problem.
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Moschopoulos, C., Laenen, G., Kritikos, G., Moreau, Y. (2012). Applying Kernel Methods on Protein Complexes Detection Problem. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_47
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DOI: https://doi.org/10.1007/978-3-642-32909-8_47
Publisher Name: Springer, Berlin, Heidelberg
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