Automatic classification of chromosomes by means of quadratically asymmetric statistical distributions

摘要

We use quadratically asymmetric distributions as introduced in Ritter [1, 2] as statistical models of human chromosomes for automatic constrained Bayesian classification into their 24 classes. These distributions are able to reflect asymmetries in the data. Moreover, we design algorithms for constrained classification of cells with missing and extra chromosomes (trisomies). Applied to the Edinburgh features of the large Copenhagen data set Cpr, the best classifier reported here reduces the cross-validation error rate from 2.7% (classical normal model) to 1.2% with respect to chromosomes. On the average, five out of six cells are completely correctly classified.