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

Ball, Sam TM ORCID: 0000-0001-5347-8647, Celik, Numan, Sayari, Elaheh, Abdul Kadir, Lina, O'Brien, Fiona and Barrett-Jolley, Richard ORCID: 0000-0003-0449-9972 (2022) DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks. PloS one, 17 (5). e0267452-.

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Official URL: http://dx.doi.org/10.1371/journal.pone.0267452

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-to-end pipeline for generating an unlimited amount of labelled training data from a small, annotated ion channel "seed" record, and this needs no prior knowledge of theoretical dynamical ion channel properties. Our method utilises 2D CNNs to maintain the synchronised temporal relationship between the raw and idealised record. We demonstrate the applicability of the method with 5 different data sources and show authenticity with t-SNE and UMAP projection comparisons between real and synthetic data. The model would be easily extendable to other time series data requiring parallel labelling, such as labelled ECG signals or raw nanopore sequencing data.

Item Type:	Article
Uncontrolled Keywords:	Humans, Algorithms, Image Processing, Computer-Assisted, Information Storage and Retrieval, Machine Learning, Neural Networks, Computer
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	30 May 2022 13:33
Last Modified:	18 Jan 2023 21:00
DOI:	10.1371/journal.pone.0267452
Open Access URL:	https://journals.plos.org/plosone/article?id=10.13...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3155635