Academic Journal
Application of ensemble machine learning methods to multidimensional AFM data sets.
| Τίτλος: | Application of ensemble machine learning methods to multidimensional AFM data sets. |
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| Συγγραφείς: | Dokukin, Maxim1 (AUTHOR) MEDokukin@mephi.ru, Dokukina, Irina1 (AUTHOR) |
| Πηγή: | Procedia Computer Science. 2020, Vol. 169, p763-766. 4p. |
| Θεματικοί όροι: | Machine learning, Atomic force microscopy, Data reduction, Electronic data processing |
| Περίληψη: | 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] |
| Βάση Δεδομένων: | Supplemental Index |
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