SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

TL;DR

SUBIC is a novel supervised biclustering method that uses convex optimization to identify patient subgroups and prioritize risk factors, enhancing personalized treatment strategies in precision medicine, demonstrated on hypertension in African-American populations.

Contribution

It introduces a new convex optimization-based supervised biclustering approach for subgroup detection and risk factor prioritization in precision medicine.

Findings

Successfully identified patient subgroups with clinical relevance.

Prioritized risk factors associated with hypertension in African-American patients.

Demonstrated effectiveness of SUBIC in a real-world medical dataset.

Abstract

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables