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AI3SD Project: Application of Capsule Net for automated DNA sequencing using tunnelling spectroscopy

AI3SD Project: Application of Capsule Net for automated DNA sequencing using tunnelling spectroscopy
AI3SD Project: Application of Capsule Net for automated DNA sequencing using tunnelling spectroscopy
In this project, we take the next step for quantum tunnelling-based biosensing and sequencing to become promising contender for label-free ‘next-next’ generation sequencing of biopolymers.This could be achieved by combining state-of-the-art surface chemistry, nanoscience and chemical sensing with Capsule Nets (CN) as a novel Deep Learning methodology, to maximise the extraction of information from the tunnelling data. Our results to date show a moderate improvement in detection accuracy when using Capsule Nets, compared to Convolutional Neural Networks. CapsNets were however superior when dealing with incomplete data, for example
when recognising events at the edges of the recording window, which could partly offset the increased computational cost. Ongoing work beyond this project now aims to further explore this aspect and support our findings with increasing amounts of experimental data. In addition, the project has allowed us to explore a new approach to data classification of single-molecule charge transport data using Transfer Learning and Image Recognition Networks (IRN). In this context, the feature extractor of IRNs trained on millions of (unrelated) image data is used to recognise characteristics in conductance-distance or current-time data, which are then clustered and interpreted. This means that no charge transport data are required for training the feature extractor, removing the need for large, application-specific training data.
AI3SD, Funded Project, Capsule Nets, Machine Learning, Neural Networks
1
University of Southampton
Albrecht, Tim
94446f17-f189-45d4-852a-a5a5c0a6d2e3
Vladyka, Anton
f1c4dac9-55ae-4d6e-922e-2e10f06addec
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Albrecht, Tim
94446f17-f189-45d4-852a-a5a5c0a6d2e3
Vladyka, Anton
f1c4dac9-55ae-4d6e-922e-2e10f06addec
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Albrecht, Tim and Vladyka, Anton , Kanza, Samantha, Frey, Jeremy G. and Hooper, Victoria (eds.) (2020) AI3SD Project: Application of Capsule Net for automated DNA sequencing using tunnelling spectroscopy (AI3SD-Project-Series, 1) University of Southampton 6pp. (doi:10.5258/SOTON/P0036).

Record type: Monograph (Project Report)

Abstract

In this project, we take the next step for quantum tunnelling-based biosensing and sequencing to become promising contender for label-free ‘next-next’ generation sequencing of biopolymers.This could be achieved by combining state-of-the-art surface chemistry, nanoscience and chemical sensing with Capsule Nets (CN) as a novel Deep Learning methodology, to maximise the extraction of information from the tunnelling data. Our results to date show a moderate improvement in detection accuracy when using Capsule Nets, compared to Convolutional Neural Networks. CapsNets were however superior when dealing with incomplete data, for example
when recognising events at the edges of the recording window, which could partly offset the increased computational cost. Ongoing work beyond this project now aims to further explore this aspect and support our findings with increasing amounts of experimental data. In addition, the project has allowed us to explore a new approach to data classification of single-molecule charge transport data using Transfer Learning and Image Recognition Networks (IRN). In this context, the feature extractor of IRNs trained on millions of (unrelated) image data is used to recognise characteristics in conductance-distance or current-time data, which are then clustered and interpreted. This means that no charge transport data are required for training the feature extractor, removing the need for large, application-specific training data.

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More information

Published date: 22 June 2020
Keywords: AI3SD, Funded Project, Capsule Nets, Machine Learning, Neural Networks

Identifiers

Local EPrints ID: 446267
URI: http://eprints.soton.ac.uk/id/eprint/446267
PURE UUID: 9a51a49b-d410-4643-962b-5496c3facc73
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 02 Feb 2021 17:52
Last modified: 25 Aug 2021 01:54

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Contributors

Author: Tim Albrecht
Author: Anton Vladyka
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Victoria Hooper

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