Abstract: Abstract: Supervised and unsupervised methods have been used extensively to analyze genomics data, with mixed results. On one hand, new insights have led to new biological findings. On the other hand, analysis results were often not robust. Here we take a look at several such challenges from the perspectives of networks and big data. Specifically, we ask if and how the added information from a biological network helps in these challenges. We show both examples where the network added information is invaluable, and others where it is questionable. We also show that by collectively analyzing omic data across multiple studies of many diseases, robustness greatly improves.