.png){kind=icon}
.png){kind=icon}
Return
Transformer-Based Single-Cell Language Model: A Survey
.png){kind=icon}
OA
AI
DOI：10.26599/BDMA.2024.9020034.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structures and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks, such as batch correction, cell clustering, cell type annotation, gene regulatory network inference, and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers who are interested in the direction of single-cell language models.
Keywords:
language model
transformers
deep learning
single-cell data
language model
transformers
deep learning
single-cell data
Journal
IF:
6.2
Papers:
274
Citations:
1.0K
Organization
No organization information available