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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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相关实验视频

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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基于RNA测序数据的泛癌分类的机器学习.

Paula Štancl1, Rosa Karlić1

  • 1Bioinformatics Group, Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia.

Frontiers in molecular biosciences
|November 29, 2023
PubMed
概括

本综述评估了用于预测癌症的机器学习方法.

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 未知原发性癌症 (CUP) 占恶性瘤的2%-5%,这给诊断和治疗带来了挑战.
  • 精确的组织起源 (TOO) 确定对于选择最佳癌症疗法至关重要.
  • 大规模的癌症基因组学计划产生了大量的数据集,用于开发预测模型.

研究的目的:

  • 通过使用RNA测序数据,审查和评估机器学习 (ML) 方法来预测癌症组织起源 (TOO).
  • 评估基于ML的TOO预测的可复制性,可解释性和稳定性.
  • 识别数据集中的潜在问题,并建议改善临床实用性.

主要方法:

  • 对20项最近使用ML进行TOO预测的研究进行系统审查.
  • 基于RNA测序数据的ML方法的分析.
  • 在独立数据集和特征识别上评估模型性能.

主要成果:

  • 评估了TOO预测的各种ML方法的性能,可重现性和可解释性.
  • 确定了不同ML方法及其基础数据集的优缺点.
  • 突出了数据集质量和模型通用性的潜在挑战.
关键词:
有关RNA测序的RNA测序癌症分类 癌症分类 癌症分类不知原因的原发性癌症.机器学习是机器学习.原产地组织原产地组织

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结论:

  • 机器学习显示出从RNA测序数据预测癌症组织起源的前景.
  • 需要进一步的研究来提高模型的稳定性,可解释性和临床适用性.
  • 解决数据集的局限性对于在临床环境中可靠的TOO预测至关重要.