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Application of ensemble machine learning methods to multidimensional AFM data sets.

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Title: Application of ensemble machine learning methods to multidimensional AFM data sets.
Authors: Dokukin, Maxim1 (AUTHOR) MEDokukin@mephi.ru, Dokukina, Irina1 (AUTHOR)
Source: Procedia Computer Science. 2020, Vol. 169, p763-766. 4p.
Subject Terms: Machine learning, Atomic force microscopy, Data reduction, Electronic data processing
Abstract: Multidimensional data sets collected with atomic force microscopy on complex biological objects like cells or tissues could be extremely informative. However, due to multidimensionality and unavailability of a large number of samples, processing of such data could be a challenge for automated machine learning methods. Here we discuss an approach based on a reduction of data dimensionality when only a limited number of parameters calculated from each microscopy map are used for machine learning algorithms. This method requires a smaller number of imaged cells, demonstrates higher accuracy of prediction, and provides cell identification that is independent of operator involvement. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
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  – Url: https://www.doi.org/10.1016/j.procs.2020.02.168?
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  Data: Application of ensemble machine learning methods to multidimensional AFM data sets.
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  Data: <searchLink fieldCode="AR" term="%22Dokukin%2C+Maxim%22">Dokukin, Maxim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> MEDokukin@mephi.ru</i><br /><searchLink fieldCode="AR" term="%22Dokukina%2C+Irina%22">Dokukina, Irina</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Procedia+Computer+Science%22">Procedia Computer Science</searchLink>. 2020, Vol. 169, p763-766. 4p.
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– Name: Abstract
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  Data: Multidimensional data sets collected with atomic force microscopy on complex biological objects like cells or tissues could be extremely informative. However, due to multidimensionality and unavailability of a large number of samples, processing of such data could be a challenge for automated machine learning methods. Here we discuss an approach based on a reduction of data dimensionality when only a limited number of parameters calculated from each microscopy map are used for machine learning algorithms. This method requires a smaller number of imaged cells, demonstrates higher accuracy of prediction, and provides cell identification that is independent of operator involvement. [ABSTRACT FROM AUTHOR]
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        Value: 10.1016/j.procs.2020.02.168
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        Text: English
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        Type: general
      – SubjectFull: Atomic force microscopy
        Type: general
      – SubjectFull: Data reduction
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              Text: 2020
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              Value: 169
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