Relative Relations in Biomedical Data Classification

Abstract

Advances in data science continue to improve the precision of biomedical research, and machine learning solutions are increasingly enabling the integration and exploration of molecular data. Recently, there is a strong need for “white box,” a comprehensive machine learning model that may actually reveal and evaluate patterns with diagnostic or prognostic value in omics data. In this article, the authors focus on algorithms for biomedical analysis in the field of explainable artificial intelligence. In particular, they present computational methods that address the concept of relative expression analysis (RXA). The classification algorithms that apply this idea access the interactions among genes/molecules to study their relative expression (i.e., the ordering among the expression values, rather than their absolute expression values). One then searches for characteristic perturbations in this ordering from one phenotype to another. They cover the concept of RXA, challenges of biomedical data analysis, and the innovations that the use of relative relationship-based algorithms brings.