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Table 1 Comparison of cell cycle phase classification accuracy obtained with different classification models (columns) and feature reduction techniques (rows).

From: Cell cycle phase classification in 3D in vivo microscopy of Drosophila embryogenesis

Accuracy SVM PNN KNN BPNN
Original features 93.52±0.62% 91.67±0.69% 90.18±0.56% 89.97±0.65%
PCA 92.45±0.73% 90.12±0.67% 90.02±0.54% 89.82±0.64%
LDA 93.12±0.45% 89.94±0.70% 89.12±0.56% 88.54±0.54%
MDS 93.23±0.44% 91.12±0.56% 91.34±0.65% 90.22±0.65%
  1. The training set consisted of 3119 samples in 5 cell cycle phases (see breakdown in Table 2) of the post-cellular blastoderm (gastrulation). Training was performed using 10 fold cross validation. SVM=support vector machine, PNN= probabilistic neural network, KNN=k nearest neighbour, BPNN=neural network with back propagation, PCA=principal component analysis, LDA=linear discriminant analysis, MDS=multidimensional scaling.