English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Single-cell RNA sequencing (scRNA-seq) provides a high throughput,quantitative and unbiased framework for scientists in many research fields toidentify and characterize cell types within heterogeneous cell populations fromvarious tissues. However, scRNA-seq based identification of discrete cell-typesis still labor intensive and depends on prior molecular knowledge. Artificialintelligence has provided faster, more accurate, and user-friendly approachesfor cell-type identification. In this review, we discuss recent advances incell-type identification methods using artificial intelligence techniques basedon single-cell and single-nucleus RNA sequencing data in vision science.

Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science