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  • 1.
    Pham, Tuan D
    et al.
    James Cook University, Townsville, QLD 4811, Australia..
    Beck, Dominik
    University of Applied Sciences Weihenstephan Weihenstephan, 85350 Freising, Germany.
    Crane, Denis I
    Nathan Campus, Griffith University, QLD 4111, Australia..
    Hidden Markov Models for Unaligned DNA Sequence Comparison2005In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, E-ISSN 2224-2902, Vol. 2, p. 64-69Article in journal (Refereed)
    Abstract [en]

    Comparison of similarity between sequences can provide information for inferring the function of a newly discovered sequence, and understanding the evolutionary relationships among genes, proteins, and entire species. This paper presents a technique for computing the similarity between unaligned DNA sequences. The computation is based on the Kullback-Leibler divergence of hidden Markov models. We used the data sets taken from the threonine operons of Escherichia coli K-12 and Shigella flexneri to test the proposed method. The result obtained agrees with an alignment-based method. We further tested the proposed method with a data set of 34 complete mammalian mtDNA genomes. The phylogenetic tree derived from the second experiment shows reasonable evolutionary relationships between these species.

  • 2.
    Tran, Dat T
    et al.
    School of Information Sciences and Engineering University of CanberraC ACT 2601 AUSTRALIA.
    Pham, Tuan D
    Bioinformatics Applications Research Centre School of Information Technology James Cook University Townsville, Australia.
    A combined Markov and noise clustering modeling method for cell phase classification2006In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, E-ISSN 2224-2902, Vol. 3, no 3, p. 161-166Article in journal (Refereed)
    Abstract [en]

    This paper proposes a classification method of cell nuclei in different mitotic phases using a combined Markov and noise clustering modeling technique. The method was tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results showed that the proposed method performed better than the k-means modeling method.

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