Abstract
In our previous work, we explored the use of graph-theoretic spectral methods for clustering protein sequences [7]. The nodes of the graph represent a set of proteins to be clustered into families and/or super-families. Edges between nodes are undirected and weighted by the similarities between proteins. We constructed a novel similarity function based on BLAST scores. The similarity values are in turn used to construct a Markov matrix representing transition probabilities between every pair of connected proteins. By analyzing the perturbations to the stationary distribution of the Markov matrix (as in [6,4]), we partition the graph into clusters. In this paper, we compare our method with TribeMCL, which modifies random walks, by reinforcing strong edges and pruning weak ones, such that clusters emerge naturally from the graph [3]. We compare these two methods with respect to their ease of use and the quality of the resulting clusters.
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Chennubhotla, C., Paccanaro, A. (2003). Markov Analysis of Protein Sequence Similarities. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_31
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DOI: https://doi.org/10.1007/978-3-540-45216-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20227-1
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