All DeepND models are trained and tested on a Super-Micro Super-Server 4029GP-TRT with 2 Intel Xeon Gold 6140 Processors (2.3 GHz, 24.75 M cache), 251 GB RAM, 6 NVIDIA GeForce RTX 2080 Ti (11 GB, 352 bit), and 2 NVIDIA TITAN RTX GPUs (24 GB, 384 bit). For DeepND-ST, we used 32,080 RTX and 1 TITAN RTX cards. For DeepND, we used 5 RTX 2080 and one TITAN RTX cards.
The running time of DeepND depends on the number, size (number of nodes), and density (number of edges) of the networks used. Note that the number of input genes and the features are constant for each condition so that it is independent of the considered disease. When we used the 3 PPI networks to co-analyze breast and ovarian cancers, DeepND took approximately 3 h. When we used the 52 co-expression networks, it took approximately 16 h.
The running time of DeepND depends on the number, size (number of nodes), and density (number of edges) of the networks used. Note that the number of input genes and the features are constant for each condition so that it is independent of the considered disease. When we used the 3 PPI networks to co-analyze breast and ovarian cancers, DeepND took approximately 3 h. When we used the 52 co-expression networks, it took approximately 16 h.