| Literature DB >> 34953466 |
Bo Zhang1, Jianghua He1, Jinxiang Hu1, Devin C Koestler1, Prabhakar Chalise1.
Abstract
Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.Entities:
Keywords: disease heterogeneity; mixture modeling; supervised learning
Mesh:
Year: 2022 PMID: 34953466 PMCID: PMC8769908 DOI: 10.1093/bib/bbab532
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 13.994