Abstract
Here we introduce a new Multi-Stack (MS) based phylogenetic tree building method. The Multi-Stack approach organizes the candidate subtrees (i.e. those having same number of leaves) into limited priority queues, always selecting the K-best subtrees, according to their distance estimation error. Using the K-best subtrees our method iteratively applies a novel subtree joining strategy to generate candidate higher level subtrees from the existing low-level ones. This new MS method uses the Constrained Least Squares Criteria (CLSC) which guarantees the non-negativity of the edge weights.
The method was evaluated on real-life datasets as well as on artificial data. Our empirical study consists of three very different biological domains, and the artificial tests were carried out by applying a proper model population generator which evolves the sequences according to the predetermined branching pattern of a randomly generated model tree. The MS method was compared with the Unweighted Pair Group Method (UPGMA), Neighbor-Joining (NJ), Maximum Likelihood (ML) and Fitch-Margoliash (FM) methods in terms of Branch Score Distance (BSD) and Distance Estimation Error (DEE). The results show clearly that the MS method can achieve improvements in building phylogenetic trees.
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Busa-Fekete, R., Kocsor, A., Bagyinka, C. (2007). A Multi-Stack Based Phylogenetic Tree Building Method. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_5
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DOI: https://doi.org/10.1007/978-3-540-72031-7_5
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