Learning to align: a statistical approach

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.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.P