As in the original D–GEX paper [6], the data for most experiments were split into training and evaluation sets randomly; however, the data used contains different sets of samples that originated in the same experiment; thus such a split might have introduced bias to the reported results. To show that such bias, if present, is insignificant for our comparison, we have also run an experiment comparing our D–GEX reimplementation with D–GEX with TAAFs on a dataset, where the split was GEO- series aware (heterogeneity–aware dataset). We grouped the available samples from the full dataset by their GEO- series, if such a grouping was obtainable from the sample id. The we performed the split such that no group would have a sample in more than one split, which removed the potential information leakage between the splits. This resulted in a subset of the normalized data used consisting of 87,345 samples (the series information was not obtainable from the sample id for some samples) split into training (52,407 samples), testing (17,469 samples), and validation (17,469 samples) sets with no series overlaps. Since the lower amount of samples available for training might negatively influence the training and the resulting model performance and since it resembles the approach of [6], most of the experiments were done using the full dataset and the heterogeneity–aware dataset was used only to verify that the model performance is not due to the bias caused by information leakage between the sets.

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