Abstract
In this paper we present an approach based on integrated use of graph clustering and visualisation methods for semi-supervised discovery of biologically significant features in biomolecular data sets. We describe several clustering algorithms that have been custom designed for analysis of biomolecular data and feature an iterated two step approach involving initial computation of thresholds and other parameters used in clustering algorithms, which is followed by identification of connected graph components, and, if needed, by adjustment of clustering parameters for processing of individual subgraphs.
We demonstrate the applications of these algorithms to two concrete use cases: (1) analysis of protein coexpression in colorectal cancer cell lines; and (2) protein homology identification from, both sequence and structural similarity, data.
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The research was supported by ERDF project 1.1.1.1/16/A/135.
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Celms, E. et al. (2018). Application of Graph Clustering and Visualisation Methods to Analysis of Biomolecular Data. In: Lupeikiene, A., Vasilecas, O., Dzemyda, G. (eds) Databases and Information Systems. DB&IS 2018. Communications in Computer and Information Science, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-319-97571-9_20
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DOI: https://doi.org/10.1007/978-3-319-97571-9_20
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