Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.
Keywords: Gene dependencies; Machine learning.