DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.

ABSTRACT: Development of automated analysis tools for "single ion channel" recording is hampered by the lack of available training data. For machine learning based tools, very large training sets are necessary with sample-by-sample point labelled data (e.g., 1 sample point every 100microsecond). In an experimental context, such data are labelled with human supervision, and whilst this is feasible for simple experimental analysis, it is infeasible to generate the enormous datasets that would be necessary for a big data approach using hand crafting. In this work we aimed to develop methods to generate simulated ion channel data that is free from assumptions and prior knowledge of noise and underlying hidden Markov models. We successfully leverage generative adversarial networks (GANs) to build an end-

SUBMITTER: Ball STM 

PROVIDER: S-EPMC9089889 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature