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Table 1 Model performance with both developmental brain gene expression features and RNA transcript sequence features

From: Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

Model Metric* Expression features Expression and sequence features
LR ROC AUC 0.8289 ± 0.0030 0.8313 ± 0.0024
  PR AUC 0.7096 ± 0.0051 0.7177 ± 0.0044
  Accuracy 0.7615 ± 0.0059 0.7563 ± 0.0054
  Sensitivity 0.7377 ± 0.0071 0.7498 ± 0.0074
  Specificity 0.7719 ± 0.0087 0.7602 ± 0.0087
  MCC 0.4723 ± 0.0095 0.4697 ± 0.0079
SVM ROC AUC 0.8232 ± 0.0049 0.8217 ± 0.0066
  PR AUC 0.7085 ± 0.0056 0.7159 ± 0.0081
  Accuracy 0.7746 ± 0.0052 0.7745 ± 0.0076
  Sensitivity 0.7111 ± 0.0115 0.7035 ± 0.0156
  Specificity 0.7999 ± 0.0094 0.8023 ± 0.0134
  MCC 0.4826 ± 0.0087 0.4789 ± 0.0123
RF ROC AUC 0.8187 ± 0.0052 0.8258 ± 0.0053
  PR AUC 0.6908 ± 0.0075 0.7008 ± 0.0113
  Accuracy 0.7767 ± 0.0196 0.7699 ± 0.0188
  Sensitivity 0.6478 ± 0.0468 0.6981 ± 0.0497
  Specificity 0.8271 ± 0.0433 0.7986 ± 0.0401
  MCC 0.4644 ± 0.0207 0.4711 ± 0.0200
  1. *The mean ROC AUC, PR AUC, overall accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) of the models from 50 repetitions of tenfold cross-validations are shown