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AI3SD Video: Event detection in single-molecule data – how to find molecular signatures without (too many) prior assumptions

AI3SD Video: Event detection in single-molecule data – how to find molecular signatures without (too many) prior assumptions
AI3SD Video: Event detection in single-molecule data – how to find molecular signatures without (too many) prior assumptions
Data from single-molecule experiments, such as from current-time or conductance-distance spectroscopy or sensors, are often “noisy” and characterised by complex molecular behaviour. In some cases, extracting the physically relevant information may be based on supervised approaches, i.e. where labelled data are available for training. In other cases, such data are either not available or it may simply be undesirable to make a priori assumptions about the molecular characteristics, for example to prevent loss of information and expectation bias.[1,2] This may require unsupervised methods or alternative approaches that put an emphasis on “what is not background?”, rather than “what does an event look like?”. In my talk, I will discuss some of the approaches we have taken, including some based on image recognition networks (AlexNet, VGG16),[3,4] and show those can be used to extract not only physically meaningful characteristics, but also previously unknown molecular behaviour.

[1] M. Lemmer et al., “Unsupervised vector-based classification of single-molecule charge transport data”, Nat. Commun. 2016, 7, art. no. 12922
[2] T. Albrecht et al., “Deep learning for single-molecule science”, Nanotechnol. 2017, 28, 423001.
[3] A. Vladyka, T. Albrecht, “Unsupervised classification of single-molecule data with autoencoders and transfer learning”, Machin. Learn.: Sci. Technol. 2020, 1, 035013.
[4] C. Weaver et al., “Unsupervised Classification of Voltammetric Data with Image Recognition and Dimensionality Reduction” (in preparation)
Albrecht, Tim
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Frey, Jeremy G.
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Kanza, Samantha
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Niranjan, Mahesan
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Albrecht, Tim
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Albrecht, Tim (2022) AI3SD Video: Event detection in single-molecule data – how to find molecular signatures without (too many) prior assumptions. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0199).

Record type: Conference or Workshop Item (Other)

Abstract

Data from single-molecule experiments, such as from current-time or conductance-distance spectroscopy or sensors, are often “noisy” and characterised by complex molecular behaviour. In some cases, extracting the physically relevant information may be based on supervised approaches, i.e. where labelled data are available for training. In other cases, such data are either not available or it may simply be undesirable to make a priori assumptions about the molecular characteristics, for example to prevent loss of information and expectation bias.[1,2] This may require unsupervised methods or alternative approaches that put an emphasis on “what is not background?”, rather than “what does an event look like?”. In my talk, I will discuss some of the approaches we have taken, including some based on image recognition networks (AlexNet, VGG16),[3,4] and show those can be used to extract not only physically meaningful characteristics, but also previously unknown molecular behaviour.

[1] M. Lemmer et al., “Unsupervised vector-based classification of single-molecule charge transport data”, Nat. Commun. 2016, 7, art. no. 12922
[2] T. Albrecht et al., “Deep learning for single-molecule science”, Nanotechnol. 2017, 28, 423001.
[3] A. Vladyka, T. Albrecht, “Unsupervised classification of single-molecule data with autoencoders and transfer learning”, Machin. Learn.: Sci. Technol. 2020, 1, 035013.
[4] C. Weaver et al., “Unsupervised Classification of Voltammetric Data with Image Recognition and Dimensionality Reduction” (in preparation)

Video
ai4sd_march_2022_day_2_TimAlbrecht - Version of Record
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Published date: 2 March 2022
Additional Information: Tim studied Chemistry at the University of Essen in Germany from 1995-2000. Following brief research visits at the European Joint Research Centre in Ispra in Italy and the University of California at Berkeley, Tim graduated with a Diploma in Chemistry (equivalent to a Masters degree) in early 2000. After graduating, Tim joined Peter Hildebrandt’s group at the Max-Planck Institute for Radiation Chemistry (now Bioinorganic Chemistry) in 2000. Tim worked on charge transfer processes in natural and artificial heme proteins on metal surfaces using SER(R)S, single-crystal electrochemistry and electrochemical STM (in Jens Ulstrup’s group at the Technical Institute of Denmark (DTU). He obtained his PhD from the Technical University (TU) of Berlin in 2003 and afterwards returned to Ulstrup’s group as a postdoctoral fellow. In 2006, he moved to London to take up a lecturer position in Interfacial and Analytical Sciences in the Chemistry Department at Imperial College, where he was made Senior Lecturer in 2011 and then Reader in 2014. In 2017, Tim joined the faculty in the School of Chemistry at Birmingham University as Chair of Physical Chemistry and has been the School of Chemistry’s Director of Research since 2018. He is the coordinator of the School’s Interest Group “Data and Machine Intelligence”.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 468641
URI: http://eprints.soton.ac.uk/id/eprint/468641
PURE UUID: abb5d29a-5822-479d-9e7f-a4061f418958
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

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Date deposited: 19 Aug 2022 16:35
Last modified: 20 Aug 2022 01:54

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Contributors

Author: Tim Albrecht
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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