Using the Pattern Discovery Engine™ applied against breast cancer datasets, we derived multiple 3-way candidate gene-gene interactions to be considered for lab testing. If validated, they could indicate a new approach to increasing breast cancer survival rates by regulating expression levels of the trio.
Using the Pattern Discovery Engine™ coupled with a hypothesis-free approach, we analyzed a large dataset of 50 human microbiome samples, each with the relative abundance of the ~10,000 KEGG protein families. We identified 39 KEGGs that were significant in differentiating the disease states from each other and from healthy, with 9 of the KEGGs (out of 10K total) being most associated with a dynamic path from disease to health in the human-gut microbiome. Using our approach we were able to reduce the size of the dataset to be analyzed by three orders of magnitude.