Learning to Align: A Statistical Approach

Elisa Ricci, Tijl De Bie, Nello Cristianini

Research output: Contribution to journalArticle (Academic Journal)peer-review


We present a new machine learning approach to the inverse parametric sequence alignment problem: given as training examples a set of correct pairwise global alignments, find the parameter values that make these alignments optimal. We consider the distribution of the scores of all incorrect alignments, then we search for those parameters for which the score of the given alignments is as far as possible from this mean, measured in number of standard deviations. This normalized distance is called the Z-score in statistics. We show that the Z-score is a function of the parameters and can be computed with ecient dynamic programs similar to the Needleman-Wunsch algorithm. We also show that maximizing the Z-score boils down to a simple quadratic program. Experimental results demonstrate the effectiveness of the proposed approach.
Translated title of the contributionLearning to Align: A Statistical Approach
Original languageEnglish
Pages (from-to)25-36
JournalIDA 2007
Publication statusPublished - 2007

Bibliographical note

ISBN: 9783540748243
Publisher: Springer
Name and Venue of Conference: IDA 2007
Other identifier: 2000793


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