Photo by Adrian Salavaty


High-throughput sequencing technologies have transformed our ability to interrogate biological systems, switching from single gene studies towards generating data on thousands of genes simultaneously. While the latter allows a systematic understanding of the genetic networks driving specific biological functions, functional biological validation of these predicted networks remains a necessity. However, prioritization of candidate genes for functional validation remains a difficult exercise, due to the lack of effective computational tools that can accurately and effectively prioritize the essential genes within a network. We developed ExIR—Experimental data-based Integrative Ranking—which enables the extraction, classification and prioritization of candidates from high-throughput experimental data. ExIR is a standalone computational model able to simultaneously classify genes into “drivers”, “biomarkers”, and “mediators” according to their functional importance. You can also visualize the ExIR results using its visualization function. This model is available in both R and Python versions of the influential package. The tutorial video showcases the function performance in the R environment.
Adrian Salavaty
Adrian Salavaty
Senior Bioinformatician
(Senior Scientist)

My research interests include Bioinformatics, Systems Biology, Graph-based Model Development, and Multi-omics Cancer Analysis.