Identification of monotonically classifying pairs of genes for ordinal disease outcomes

We extend an existing classification method for identifying pairs of genes whose joint expression is associated with binary outcomes to ordinal multi-class outcomes, such as overall survival or disease progression. Our approach, called multi-class bivariate monotonic classifiers (MBMC), is motivated by the need for interpretable classifiers that can provide insights into the underlying biological mechanisms. It can be easily adapted to different research questions, such as identifying gene pair signatures or functional enrichment. We demonstrate that our method is comparable to state-of-the-art classification approaches in terms of performance while offering the benefit of higher interpretability and adaptability to solve different research questions. Our evaluation on three real-world use cases in glioblastoma, high-grade serous ovarian carcinoma, and breast cancer shows that our approach can effectively predict ordinal outcomes and provide novel biological insights.

The code is available at https://github.com/oceanefrqt/MBMC.