We consider the problem of mixed linear regression (MLR), where each observed sample
belongs to one of $ K $ unknown linear models. In practical applications, the mixture of the …

Revealing relationships between genes and disease phenotypes is a critical problem in
biomedical studies. This problem has been challenged by the heterogeneity of diseases …

Matrix low rank approximation is an effective method to reduce or eliminate the statistical
redundancy of its components. Compared with the traditional global low rank methods such …

Mixed linear regression (MLR) is a powerful model for characterizing nonlinear relationships
by utilizing a mixture of linear regression sub-models. The identification of MLR is a …

Lung cancer subtyping based on gene expression data is important for identifying patient
subgroups with differing survival prognosis to facilitate customized treatment strategies for …

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models
carry various genetic and physiological perturbations, making it questionable to assume …

Motivation Mixture regression has been widely used as a statistical model to untangle the
latent subgroups of the sample population. Traditional mixture regression faces challenges …

Low rank representation of a matrix can reduce or eliminate the statistical redundancy
among its components, and enable a lower dimensional representation without significant …

Abstract Component-wise Sparse Mixture Regression (CSMR) is a recently proposed
regression-based clustering method that shows promise in detecting heterogeneous …

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 …