A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.

ABSTRACT: Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label conf

SUBMITTER: Li D 

PROVIDER: S-EPMC10070828 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature