Conserved Machine Learning Rankings of Myc Gene Combinations Across Different Sensitivity Methods Connote Existence of Biological Synergy

A recent design of a machine learning based search engine was published, that ranks combinations of genes that might be working synergistically in cells in various processes. To demonstrate its efficacy in real life scenario, the data set containing recordings of up/down regulated genes generated from colorectal cancer (CRC) cells treated with PROCN-WNT inhibitor drug ETC-1922159 was taken. The regulation of the genes were recorded individually, but in many cases, it is still not known which higher (≥ 2) order gene combinations might be playing a greater role in CRC. Here, I demonstrate that the rankings assigned to gene combinations at 2nd order, by the search engine are conserved across the different sensitivity methods (and kernels/variants). This conservation points to the possible existence of the synergy between genes at the biological level. To establish the hypothesis I present ranked combinations of v-myc avian myelocytomatosis viral oncogene homolog (MYC), known to encode proteins that play significant role as transcription factors in cancer and target various kinds of genes, thus contributing to regrowth and proliferation. The manuscript identifies experimentally tested combinations of MYC-X in literature (whether in CRC cell or other cancer/ordinary cell). Second, the work reveals machine learning rankings for these MYC-X combinations in ETC-1922159 treated CRC cells. For experimentally established combinations, these rankings bolster confirmatory results. Based on the second step, the work points to new rankings of unknown/untested/unexplored MYC-X com- binations that might be working synergistically in CRC cells.