论文标题
基于视力科学中单细胞RNA测序数据的细胞类型和亚型识别的人工智能模型
Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science
论文作者
论文摘要
单细胞RNA测序（SCRNA-SEQ）为许多研究领域的科学家提供了高吞吐量，定量和无偏见的框架，以识别和表征来自各种组织中异质细胞种群中的细胞类型。但是，基于SCRNA-SEQ的离散细胞类型的鉴定仍然是劳动密集型的，并且取决于先前的分子知识。人工智能为细胞类型识别提供了更快，更准确和用户友好的方法。在这篇综述中，我们使用基于视觉科学中单细胞和单核RNA测序数据的人工智能技术讨论了细胞类型识别方法的最新进展。
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science.
