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Sorting of Single–Molecule Trajectories by means of Machine Learning - a status update on the annotation procedure

  • We use machine learning for the selection and classification of single–molecule trajectories to replace commonly used user–dependent sorting algorithms. Measured fluorescence time series of labelled single molecules need to be sorted into ’good molecules’ and ’bad’ molecules before further kinetic and thermodynamic analysis. Currently, processing, sorting and analysis of the data is mainly done with the help of laboratory specific programs. Although there are freely available programs for processing smFRET data, they do not offer ’molecular sorting’ or it is purely empirical. Only recently, new approaches came up to solve this problem by means of machine learning. Here, we describe a sound terminology for molecular sorting of smFRET data and present an efficient workflow for manual annotation followed by the training of the ML algorithm. Descriptive statistics of our generated dataset are provided and will serve as the basis for supervised ML-based molecular sorting algorithms yet to be developed.

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Metadaten
Author:Lisa Krenkel, Tobias Schlosser, Danny Kowerko, Richard Börner
URN:urn:nbn:de:bsz:mit1-opus4-122928
DOI:https://doi.org/10.48446/opus-12292
ISSN:1437-7624
Parent Title (German):26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida
Publisher:Hochschule Mittweida
Place of publication:Mittweida
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Publishing Institution:Hochschule Mittweida
Contributing Corporation:Laserinstitut Hochschule Mittweida
Release Date:2021/05/18
Tag:data annotation; molecular sorting,; molecule classification
GND Keyword:Fluoreszenz-Resonanz-Energie-Transfer; Maschinelles Lernen
Issue:002
Page Number:4
First Page:126
Last Page:129
Open Access:Frei zugänglich
Licence (German):License LogoUrheberrechtlich geschützt