SNRAI data scientists and software engineers develop data-driven models that leverage the surprising connection between signal sparsity and the immune status of the tumor microenvironment (TME) to better predict how a cancer patient will respond to ICB and other immunotherapies.

SNRAI is developing an internal database of immunological signatures that are likely predictors of ICB response and/or overall survival in multiple cancers based on the analysis of all cancers in The Cancer Genome Atlas using an analysis pipeline based on SSP techniques.

SNRAI is developing a cloud-based version of a SSP pipeline that runs in Jupyter notebooks to facilitate the wider use of SSP techniques in biomarker discovery and predictive modeling of cancer and other complex diseases.