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Table 1 Prediction performance of three meta-predictors using three different sets of techniques

From: Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks

Dataset

C-I

C-II

mirTarDANN

Sens

Spec

Acc

Sens

Spec

Acc

Sens

Spec

Acc

D4

0.240 ± 0.120

0.780 ± 0.128

0.735 ± 0.108

0.357 ± 0.093

0.733 ± 0.087

0.702 ± 0.072

0.568 ± 0.049

0.555 ± 0.031

0.556 ± 0.025

D3–1

0.475 ± 0.063

0.602 ± 0.036

0.584 ± 0.022

0.297 ± 0.032

0.789 ± 0.044

0.716 ± 0.031

0.602 ± 0.048

0.530 ± 0.041

0.541 ± 0.032

D3–2

0.671 ± 0.097

0.235 ± 0.108

0.268 ± 0.094

0.433 ± 0.128

0.690 ± 0.041

0.669 ± 0.036

0.511 ± 0.176

0.605 ± 0.111

0.593 ± 0.092

D3–3

0.087 ± 0.018

0.693 ± 0.042

0.557 ± 0.028

0.693 ± 0.052

0.704 ± 0.069

0.701 ± 0.047

0.660 ± 0.037

0.730 ± 0.056

0.715 ± 0.037

D3–4

0.383 ± 0.058

0.434 ± 0.088

0.420 ± 0.050

0.622 ± 0.061

0.752 ± 0.030

0.713 ± 0.012

0.650 ± 0.051

0.722 ± 0.051

0.700 ± 0.026

D2–1

0.700 ± 0.073

0.221 ± 0.040

0.334 ± 0.029

0.402 ± 0.065

0.653 ± 0.040

0.593 ± 0.013

0.312 ± 0.066

0.722 ± 0.078

0.624 ± 0.037

D2–2

0.519 ± 0.049

0.444 ± 0.040

0.480 ± 0.023

0.668 ± 0.202

0.728 ± 0.017

0.701 ± 0.094

0.685 ± 0.102

0.796 ± 0.020

0.745 ± 0.038

D2–3

0.306 ± 0.137

0.307 ± 0.062

0.312 ± 0.035

0.527 ± 0.055

0.849 ± 0.019

0.738 ± 0.042

0.558 ± 0.047

0.849 ± 0.026

0.749 ± 0.017

D2–4

0.398 ± 0.024

0.659 ± 0.038

0.509 ± 0.004

0.777 ± 0.081

0.393 ± 0.182

0.608 ± 0.050

0.745 ± 0.023

0.540 ± 0.029

0.656 ± 0.023

D2–5

0.388 ± 0.175

0.188 ± 0.174

0.276 ± 0.044

0.700 ± 0.079

0.837 ± 0.060

0.779 ± 0.011

0.721 ± 0.086

0.849 ± 0.022

0.797 ± 0.050

D2–6

0.700 ± 0.155

0.255 ± 0.032

0.466 ± 0.063

0.678 ± 0.065

0.724 ± 0.048

0.700 ± 0.038

0.622 ± 0.074

0.769 ± 0.060

0.693 ± 0.017

  1. C-I integrates individual predictors with a decision tree, C-II uses ANNs to integrate individual predictors, and mirTarDANN is a combination of individual predictors, decision tree, and ANNs. The performance is measured by sensitivity (Sens), specificity (Spec), and accuracy (Acc) under multi-fold cross-validation. The high-lighted values are the highest among three meta-predictors in the same subset