Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

ABSTRACT:

Motivation

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable.

Results

We herein pro

SUBMITTER: Wan H 

PROVIDER: S-EPMC10883808 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature