A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.

ABSTRACT: Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known

SUBMITTER: Pan L 

PROVIDER: S-EPMC11756795 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature