Scatterplot of class probability vs the actual ACMG class for the TabNet model across the validation set of 156 variants. by Aastha Vatsyayan (9981916)

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The Precision-Recall curve of both the models. by Aastha Vatsyayan (9981916)

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S4 Table - by Aastha Vatsyayan (9981916)

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Train and validation accuracies of the TabNet model across 187 epochs. by Aastha Vatsyayan (9981916)

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The overview of the workflow followed for model development and variant classification with TabNet and XGBoost models. by Aastha Vatsyayan (9981916)

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S2 Table - by Aastha Vatsyayan (9981916)

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Barplot comparing the accuracy, MCC, NPV and PPV of 13 models with TabNet and XGBoost. by Aastha Vatsyayan (9981916)

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Scatterplot of class probability vs the actual ACMG class for the XGBoost model across the validation set of 156 variants. by Aastha Vatsyayan (9981916)

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Comparison of the performance of both models against 13 other models on the independent test dataset. by Aastha Vatsyayan (9981916)

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Confusion matrix depicting the models’ predictions on the 30% test data. by Aastha Vatsyayan (9981916)

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Confusion matrix of predictions made on the ACMG-qualified independent validation dataset comprising of 156 variants. by Aastha Vatsyayan (9981916)

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Scatterplot of class probability vs the predicted class for the TabNet model across all VUS variants 251 exonic VUS variants that were a part of the validation dataset. by Aastha Vatsyayan (9981916)

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The model learning rate and loss of the TabNet model across187 epochs. by Aastha Vatsyayan (9981916)

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S12 Fig - by Aastha Vatsyayan (9981916)

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Fig 2 - by Aastha Vatsyayan (9981916)

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Scenarios for algorithms using IEEE 33 bus. by Bachirou Bogno (20485724)

Scenarios for algorithms using IEEE 69 bus. by Bachirou Bogno (20485724)

AI surface plot with epochs and weighting coefficients when mini-batch size at 544. by Tsutomu Gomi (7382183)

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Diagram of the algorithm used during the training phase. by Ana P. Arantes (17131753)

SP results of algorithms in the CEC 2009 test functions. by Vahid Goodarzimehr (21343124)