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Corrections factors therefore may be dataset-specific.

We have also used a large number of experimental datasets across multiple tissues, but there may be datasets with substantially different properties that require different handling.

The dataset may be found in Dataset S1.

This suggests that the noise introduced by individual experiments in this H37Rv B2H PPI dataset may be higher than other datasets compared.

However, Y2 H datasets are more likely to contain false negatives (incompleteness of Y2 H datasets) and ePPID in such datasets may be underestimated, whereas AP/MS-related datasets are more likely to contain false positives (indirect non-physical interactions) and ePPID in such datasets may be overestimated.

In a big data environment, there may be a large dataset where only a portion of the data is related to a target domain of interest.

An explanation for the wider variation may be that our dataset was smaller.

It's not clear exactly how these three companies may be using the datasets, beyond making their own autonomous driving systems smarter and more fail-safe.

A cross-validated dataset may be the most robust estimate available, compensating for underreporting to both datasets by including all deaths registered in any of the two.

It may be due to our dataset is related with proinflammatory effects and Cui's dataset is focused on cancer signaling.

Nevertheless, different molecular datasets often show conflicting phylogenetic signals, so that results relying on just one dataset may be interpreted with caution [ 1].

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