As can be seen in Figure S2, while ZNMI and MIp preform similarly for the three datasets, Zres outperforms both algorithms for two of the three datasets (excepting the GPCR dataset).
Except for the dataset at 0 min with larger average RPKM values (discussed below), all other datasets had similar distribution patterns (Additional file 8A).
It is worth noting that the topology supporting a basal position of the mouse-related clade is again the least supported, except for the dataset with maximum rate ≤ 0.5.
There is no obvious reason why the results of the two studies should differ except that the dataset compiled for the present study was very much larger than the EFSA dataset and therefore more likely to be representative of the real situation.
We have the following observations from the experimental results: (1) Our HierSymNMF2 algorithm has significant advantages over other methods in average normalized cut on most datasets except the Amazon dataset.
Values lower than 0.5 are hard-thresholded to 0, except for the datasets from M3 D, which are thresholded at 0.8 and 0.85, respectively.
However, botnet flow percentage increases (6 8%) when there are multiple bots present in the dataset (except scenario 12).
Except the expression recognition, the dataset is split into five random partitions and experiments are performed five times.
This filters out most of the travel information in the dataset, except interruptions such as traffic jams and waiting for public transport.
The dataset (except for 131I− because of complete decay at the end of the experiment) was analyzed with different diffusion-sorption models by different teams (IDAEA-CSIC, UJV-Rez, JAEA) using different codes, with the goal of obtaining effective diffusion coefficients (De) and porosity or rock capacity values.
