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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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相关实验视频

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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规范化和选择非差异表达基因改善了跨平台转录基因数据的机器学习建模.

Fei Deng1, Catherine H Feng1,2, Nan Gao3,4

  • 1Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.

ArXiv
|February 20, 2025
PubMed
概括

非差异表达基因 (NDEG) 在转录基因数据中改善跨平台机器学习 (ML) 模型的正常化. 这种方法提高了ML模型的性能,用于使用独立的微阵列和RNA-seq数据集对乳腺癌亚型进行分类.

关键词:
乳腺癌 乳腺癌 乳腺癌功能选择 功能选择机器学习 机器学习规范化 规范化 规范化文字转录学 (Transcriptomics) 是一个学科.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 规范化对于定量生物分析至关重要.
  • 机器学习 (ML) 的RNA微阵列和RNA测序 (RNA-seq) 数据的跨平台集成是具有挑战性的,因为数据异质性.
  • 之前的研究缺乏对独立数据集的验证,这使得ML性能在未见数据上的改进不清楚.

研究的目的:

  • 测试非差异表达基因 (NDEG) 可以改善转录基因数据规范化和随后的跨平台ML模型性能的假设.
  • 用独立的数据集来评估基于NDEG的规范化对分类乳腺癌分子亚型的有效性.
  • 为了比较基于参数与非参数统计数据的规范化方法,用于跨平台的ML.

主要方法:

  • 使用癌症基因组图谱 (TCGA) 乳腺癌微阵列和RNA-seq数据集分别作为独立的培训和测试集.
  • 选择的NDEG (p>0.85) 和差异表达基因 (DEG,p<0.05) 使用ANOVA进行分别规范化和分类.
  • 在一个平台的数据上训练了ML模型,并在另一个平台上测试了LOG_QN和LOG_QNZ规范化方法.

主要成果:

  • NDEG和DEG选择显著改善了乳腺癌亚型的ML模型分类性能.
  • 非参数化规范化方法的性能优于参数化方法.
  • 与神经网络模型相结合的LOG_QN和LOG_QNZ规范化方法显示出卓越的性能.

结论:

  • 基于NDEG的规范化是改善独立转录组数据集的跨平台ML模型性能的一个有希望的策略.
  • 这种方法提高了分子亚型的分类.
  • 需要进一步的研究来验证跨不同数据集和omics类型的NDEG规范化.