LtransHeteroGGM: local transfer learning for Gaussian graphical model-based heterogeneity analysis.

ABSTRACT:

Motivation

Heterogeneity is a hallmark of both macroscopic complex diseases and microscopic single-cell distribution. Gaussian graphical models (GGMs)-based heterogeneity analysis highlights its important role in capturing the essential characteristics of biological regulatory networks, but faces instability with scarce samples from rare subgroups. Transfer learning offers promise by leveraging auxiliary data, yet existing approaches rely on unrealistic overall similarity between domains, requiring the same subgroup number and similar parameters. Numerous biological problems call for local similarities, where only some subgroups share statistical structures.

Results

In this article, we propose LtransHeteroGGM, a novel local transfer learning framework for GGM-based heterogen

SUBMITTER: Li C 

PROVIDER: S-EPMC12944826 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature