Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore

Abstract

Single nanopore is a powerful platform to detect, discriminate and identify biomacromolecules. Among the different devices, the conical nanopores obtained by the track-etched technique on a polymer film are stable and easy to functionalize. However, these advantages are hampered by their high aspect ratio that avoids the discrimination of similar samples. Using machine learning, we demonstrate an improved resolution so that it can identify short single- and double-stranded DNA (10- and 40-mers). We have characterized each current blockade event by the relative intensity, dwell time, surface area and both the right and left slope. We show an overlap of the relative current blockade amplitudes and dwell time distributions that prevents their identification. We define the different parameters that characterize the events as features and the type of DNA sample as the target. By applying support-vector machines to discriminate each sample, we show accuracy between 50% and 72% by using two features that distinctly classify the data points. Finally, we achieved an increased accuracy (up to 82%) when five features were implemented.