SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
This work addresses the need for tailored treatment schemes in precision medicine by enabling subgroup detection with clinical guidance, though it is incremental as it builds on existing biclustering and optimization techniques.
The authors tackled the problem of detecting patient subgroups for precision medicine by proposing SUBIC, a supervised biclustering method using convex optimization, and applied it to identify subgroups and prioritize risk factors for hypertension in African-American patients, achieving results that include prioritized risk factors through sparsity and similarity constraints.
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