Machine learned regression for abductive DNA sequencing

We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypotheses for sequence composition through calculation of likelihood or deviation evidence from the comparison of predictions from the hypothesized sequence with target trace data. We machine learn a means for comparing the measures taken from competing hypotheses for the sequence. This is a machine learned implementation of our proposal for abductive DNA basecalling. The results of the present experiments suggest that neural nets are a more effective means for predicting peak sizes than decision tree regressors, and for assembling evidence for competing hypotheses in this context. This is despite the availability of variance estimates in our decision tree regressors.

• Type: Conference
• Year: 2007
• Where: ICMLA
• Authors: David Thornley, Maxim Zverev, Stavros Petridis
• Keywords: Decision Tree Regressors, Hypotheses, ICMLA 2007, Machine Learned Regressors, Machine Learning