Machine learning analysis of nanopore measurements

Abstract

A series of measurements taken from a polymer during translocation through a nanopore is analysed using a machine learning technique using a recurrent neural network (RNN). The RNN may derive posterior probability matrices each representing, in respect of different respective historical sequences of polymer units corresponding to measurements prior to the respective measurement, posterior probabilities of plural different changes to the respective historical sequence of polymer units giving rise to a new sequence of polymer units. Alternatively, the RNN may output decisions on the identity of successive polymer units of the series of polymer units, wherein the decisions are fed back into the recurrent neural network. The analysis may comprise performing convolutions of groups of consecutive measurements using a trained feature detector such as a convolutional neural network to derive a series of feature vectors, on which the RNN operates.