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Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks - Fig 3
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posted on 2016-01-28, 03:14 authored by Elisa Valletta, Lukáš Kučera, Lubomír Prokeš, Filippo Amato, Tiziana Pivetta, Aleš Hampl, Josef Havel, Petr Vaňhara(A) Optimal ANN architecture (one Input layer, one Hidden layer with four neurons, and one Output layer). (B) Training and leave-one-out verification plot of the RMS versus the number of training cycles (epochs). First 50 000 iterations are shown. The inset shows a detailed plot for the first 10 000 training cycles.
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eukaryotic cell linesShort Tandem RepeatsMS analysiscell linesMultivariate Calibration ApproachANNquantifying heterogeneitySSRDNA sequencescell authenticationSimple Sequence Repeatsmass spectra databasecell culturesIntact Cell Mass SpectrometryMEFtraining inputreference databasesQuantitative Determinationcontamination levelSTRcalibration mixtures
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