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Table 2 Singapore data results

From: Fast and robust imputation for miRNA expression data using constrained least squares

Metric FLI VIPER scImpute Regression Mean Zeros
(a) Classification results
AUC .95 .92 .91 .82 .80 .76
\(F_1\) .91 .88 .88 .79 .80 .78
ACC .89 .86 .87 .76 .74 .70
(b) Imputation errors
\(\epsilon _{\mu }\) .35 .41 .45 .73 .75 .70
\(\epsilon _{\sigma }\) .25 .34 .39 .54 .43 .06
\(\epsilon _{M}\) 1.12 1.94 2.50 3.53 1.85 .90
Time FLI VIPER scImpute Regression Mean Zeros
(c) Imputation time
\(t_\mu\) .009 7.29 .100 \(\sim 0\) \(\sim 0\) \(\sim 0\)
\(t_\sigma\) .002 8.42 .060 \(\sim 0\) \(\sim 0\) \(\sim 0\)
\(t_{\text {max}}\) .027 49.2 .300 .003 .001 \(\sim 0\)
  1. (a) Mean values over curves shown in Fig. 4a–c. (b) Mean values over curves shown in Fig. 4d–f. (c) Mean (\(t_\mu\)), standard deviation (\(t_\sigma\)), and maximum (\(t_{\text {max}}\)) imputation times (in seconds) over all test patients. In table (c), \(\sim\)0 indicates that the imputation time is strictly less than .0005 s