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使用深度学习技术进行单细胞分类,分析及其应用.

R Premkumar1, Arthi Srinivasan2, K G Harini Devi1

  • 1Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, 602105, India.

Bio Systems
|February 10, 2024
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 增强了针对癌症等复杂疾病的单细胞分析 (SCA). DL方法提供了卓越的数据处理和分析,克服了单细胞奥米学中传统测序技术的局限性.

关键词:
数据科学是数据科学.深度学习是一种深度学习.一个单细胞分析.单细胞分类的分类方式

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科学领域:

  • 生物技术是生物技术.
  • 基因组学就是基因组学.
  • 计算生物学 计算生物学

背景情况:

  • 单细胞分析 (SCA) 对于理解癌症和免疫疾病等疾病中复杂的生物过程至关重要.
  • 传统的细胞测序方法在信号处理和疾病检测方面面临局限性,原因是高维,复杂的数据.
  • 现有的分析方法与SCA数据的复杂性作斗争,阻碍了全面的洞察力.

研究的目的:

  • 审查深度学习 (DL) 技术在单细胞分析 (SCA) 中的应用.
  • 详细说明DL如何改善SCA的数据处理和分析.
  • 探索未来的方向,挑战和机遇,DL在快速发展的领域的单细胞奥米克.

主要方法:

  • 审查细胞分析技术中的基本概念和关键点.
  • 对应用到SCA数据的各种有效深度学习策略的讨论.
  • 探索DL在克服传统SCA限制方面的作用.

主要成果:

  • 与SCA中的传统算法相比,深度学习技术已经显示出更高的性能.
  • DL有效地分析由SCA技术生成的复杂,高维的数据.
  • 通过在SCA中的DL应用程序,可以实现数据处理和分析的显著改进.

结论:

  • 深度学习为单细胞分析数据带来的挑战提供了强大的解决方案.
  • DL是一个有前途的未来方向,用于推进单细胞体质学研究.
  • 在SCA中继续探索DL将开启新的机遇,并应对新出现的挑战.