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Table 1 Summary of quality-dependent features used for supervised learning

From: nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data

Number Feature name Short description
1 Area of IDT spikes Area of spikes appearing in the indentation part
2 Curvature at CP Curvature of the force-distance data at the contact point
3 Flatness of APR residuals Fraction of the positive-gradient residuals in the approach part
4 Maxima in IDT residuals Sum of the indentation residuals’ maxima in three intervals in-between 25% and 100% relative to the maximum indentation
5 Monotony of IDT Change of the gradient in the indentation part
6 Overall IDT residuals Sum of the residuals in the indentation part
7 Relative APR size Length of the approach part relative to the indentation part
8 Residuals at CP Mean value of the residuals around the contact point
9 Residuals in 75% IDT Sum of the residuals in the indentation part in-between 25% and 100% relative to the maximum indentation
10 Residuals of APR Absolute sum of the residuals in the approach part
11 Slope of BLN Slope obtained from a linear least-squares fit to the baseline
12 Variation in BLN Comparison of the forces at the beginning and at the end of the baseline
  1. The features are visualized in Fig. 4; Abbreviations in feature names: indentation (IDT), contact point (CP), approach (APR), baseline (BLN)
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BMC Bioinformatics

ISSN: 1471-2105

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