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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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相关实验视频

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Selective Capture of 5-hydroxymethylcytosine from Genomic DNA
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基因组数据分析的基准分析稀有变量选择方法.

Hema Sri Sai Kollipara1, Tapabrata Maiti1, Sanjukta Chakraborty2

  • 1Department of Statistics & Probability, Michigan State University, East Lansing, Michigan, USA.

Statistics in medicine
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概括
此摘要是机器生成的。

本研究将贝叶斯的变量选择方法与基因组分析进行比较. 没有任何一种方法在所有场景中都超越,但LASSO,尖和板 (SN) 和RFSFS表现出强的表现,特别是与相关的特征.

关键词:
贝叶斯的变量选择选择是贝叶斯的.的RNA序列数据数据.错误的负面结果是错误的有错误的阳性结果.预测 预测 预测 预测正规化的回归研究.

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科学领域:

  • 基因组学就是基因组学.
  • 统计遗传学 统计遗传学
  • 计算生物学 计算生物学

背景情况:

  • 基因组研究涉及许多特征,需要精确的变量选择.
  • 贝叶斯推理已经在变量选择方面取得了进展,但缺乏实际实施细节和性能比较.

研究的目的:

  • 对基因组数据进行贝叶斯变量选择方法的比较分析.
  • 评估收缩,全球-本地,混合先验,SUSIE和拟议的RFSFS方法的性能.

主要方法:

  • 贝叶斯变量选择方法的比较分析.
  • 使用诸如假发现率 (FDR),假负率 (FNR),F-score和平均平方预测错误等指标进行评估.
  • 在各种场景下进行模拟研究,包括非相关的和相关的特征.

主要成果:

  • 没有一种方法在所有场景和指标中均地胜过其他方法.
  • LASSO,spike-and-slab prior with normal slab (SN) 和RFSFS对于FDR和F-score具有不相关的特征而具有竞争力.
  • 对于具有相关特征的FDR,SN,SuSIE和RFSFS具有竞争力;LASSO在F分数方面优于SuSIE.

结论:

  • 方法性能因特征相关性和评估指标而异.
  • 拟议的RFSFS方法证明了与LASSO和SN.等既定技术相结合的竞争性性能.
  • 结果为基因组分析中的变量选择提供了方法指导,包括癌症基因组图谱 (TCGA) 数据.