A new challenge for compression algorithms: genetic sequences
Information Processing and Management: an International Journal - Special issue: data compression
Significantly Lower Entropy Estimates for Natural DNA Sequences
Significantly Lower Entropy Estimates for Natural DNA Sequences
A programming paradigm for machine learning, with a case study of Bayesian networks
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Searching a pattern in compressed DNA sequences
International Journal of Bioinformatics Research and Applications
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Populations of biased, non-random sequences may cause standard alignment algorithms to yield false-positive matches and false-negative misses A standard significance test based on the shuffling of sequences is a partial solution, applicable to populations that can be described by simple models Masking-out low information content intervals throws information away We describe a new and general method, modelling-alignment: Population models are incorporated into the alignment process, which can (and should) lead to changes in the rank-order of matches between a query sequence and a collection of sequences, compared to results from standard algorithms The new method is general and places very few conditions on the nature of the models that can be used with it We apply modelling-alignment to local alignment, global alignment, optimal alignment, and the relatedness problem.