A Monte Carlo Newton¯Raphson procedure for maximizing complex likelihoods on pedigree data

Automatic classification of the chromosomes of a metaphase eukaryotic cell under a light microscope into their biological classes is usually done in three steps: First, their centromeres are estimated in order to find their polarities, next a number of features are extracted from profiles of the oriented chromosomes, and finally the feature sets are assigned to classes. The first step is prone to errors since it is often not easy to detect the centromere. If it is determined on the wrong half of the chromosome then polarity is false leading to erroneous features in the second step and often to a misclassification. We reduce the error rate by applying the recently developed Bayesian method of variants to the profiles; applied to polarities, this method uses two feature sets for each chromosome, one for each polarity. We also take another look at feature extraction from profiles further reducing the error rate. Applied to the profiles of the Edinburgh MRC chromosome analysis system the most accurate methods reported here achieve cross-validation error rates below 1%.