Biologists/oncologists often search for a range of combinations of genes/proteins that work synergistically in cells in various processes. This search is often difficult. To address this issue, a recent design of a machine learning based search engine was published recently. To demonstrate the effectiveness of this search engine on real life data sets, the data set containing recordings of up/down regulated genes generated from colorectal cancer cells which were treated with PROCN-WNT inhibitor drug ETC- 1922159 was taken. The regulation of the genes were recorded individually, but it is still not known which higher (≥ 2) order gene combinations might be playing a greater role after the administration of the drug. In order to reveal the priority of these higher order combinations among the up/down-regulated genes in static data, I used an adaptation of the published search engine. The engine reveals unknown/untested/unexplored as well as wet lab tested combinations. Based on these rankings, biologists/oncologists would not have to struggle to discover a particular gene/protein combination of interest for further wet lab test, that might be involved in a particular phenomena.