PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning to align: a statistical approach
elisa ricci, Tijl De Bie and Nello Cristianini
In: IDA 2007, 6-8 Sep 2007, Ljubljana, Slovenia.


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 efficient 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3823
Deposited By:Tijl De Bie
Deposited On:25 February 2008