Channel current cheminformatics and stochastic carrier-wave signal processing

Abstract

Methods for channel current cheminformatics are reviewed, including finite state automaton based signal acquisition methods and generalized Hidden Markov model (HMM) methods for both signal feature extraction and structure identification. The generalized HMMs described enable a new form of carrierbased communication, where the carrier is stationary but not periodic. HMMwith-binned-duration, and meta-HMM generalizations, in particular, are shown to enable practical stochastic carrier wave encoding/decoding, where the generalized HMM methods have generalized Viterbi algorithms with all of the inherent benefits of an efficient dynamic programming implementation, as well as Martingale convergence properties when used for filtering and robust feature extraction. Applications to extracting channel current blockade signals from the nanopore transduction setting are discussed to provide a specific, challenging, application example, where individual molecular captures, generally nontranslocating, generate strongly stationary channel blockade signals, allowing stochastic carrier-wave signal processing and channel current signal transduction functionalization in a variety of settings. Stochastic carrier wave signal processing enablers improved ('smart-device') signal processing in a number of settings in science and nanotechnology.