(Invited) Machine Learning for DNA/SWCNT Based Molecular Perceptron: Finding Sequences and Training Sensor Arrays

Single wall carbon nanotube (SWCNT) based biosensors provide opportunities for building an ultra-sensitive biosensing system due to their unique optical properties and strong sensitivity to changes in the local environment. Consequently, much effort has been made to develop SWCNT-based sensors. However, the usual method is based on one-to-one recognition which is a difficult way to detect a variety of molecules. In this study, we describe a sensing system that uses an array of weakly-specific sensors combined with a machine learning model, which we call the Molecular Perceptron. We show how machine learning algorithms, along with choice of feature representation, can be used both for discovery of special resolving DNA sequences and to predict presence and concentration of biomarkers. DNA/SWCNT hybrids were utilized to optically detect biomarker analytes by observing changes in the fluorescence spectra of each SWCNT. Using the experimental data, machine learning models were trained using three different algorithms: Support Vector Machine, Random Forest, and Artificial Neural Network. We also demonstrate that machine learning models trained on relatively small DNA sequence data sets can very accurately predict new resolving DNA sequences