ScMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

ABSTRACT: Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of

SUBMITTER: Zhou S 

PROVIDER: S-EPMC10833085 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature