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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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这是PNNGS,一个用于基因组选择的多卷积并行神经网络.

Zhengchao Xie1, Lin Weng1, Jingjing He1

  • 1Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China.

Frontiers in plant science
|September 18, 2024
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概括
此摘要是机器生成的。

平行神经网络通过使用平行卷曲来提高基因组选择 (GS) 的准确性. 这种方法提高了预测的稳定性和准确性,特别是在不平衡的植物育种数据的情况下.

关键词:
深度学习是一种深度学习.基因组选择 基因组选择平行主义平行主义.植物育种 植物育种分层采样分层采样分层采样

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科学领域:

  • 植物育种 植物育种
  • 基因组学就是基因组学.
  • 机器学习是机器学习.

背景情况:

  • 基因组选择 (GS) 比传统的表型选择加快了作物和牲畜的改进.
  • 提高预测准确度对于GS的广泛采用和有效性至关重要.
  • 深度学习模型提供了改进GS的潜力,但需要对复杂的基因组数据进行优化.

研究的目的:

  • 引入一种新的深度学习模型,即基因组选择的并行神经网络 (PNNGS),以提高GS预测的准确性和稳定性.
  • 调查并行卷积路径和不同损失函数对GS性能的影响.
  • 解决基因组预测中不平衡的集群数据所带来的挑战.

主要方法:

  • 开发了PNNGS,结合了具有不同核大小和残余连接的平行卷曲.
  • 使用四个L_p损失函数训练PNNGS,并确定不同物种 (大米,向日,小麦,玉米) 的最佳并行路径数.
  • 将PNNGS与RRBLUP,RF,SVR和DNNGP进行比较,使用24个预测案例,并使用PCA和K-means进行数据集群和分层抽样.

主要成果:

  • 在表型预测准确度方面,PNNGS和串行DNNGP的表现优于RRBLUP,RF和SVR.
  • PNNGS显示了比DNNGP高0.031的平均预测准确度,证实了并行性的好处.
  • 分层采样提高了PNNGS预测稳定性和准确性,当小集群减少样本大小时,准确性大幅下降.

结论:

  • 基因组选择的并行神经网络 (PNNGS) 有效地提高了基因组选择的预测准确性和稳定性.
  • 深度学习模型中的并行性有利于提高基因组预测性能.
  • 通过分层抽样和在较小的集群中增加样本大小来解决数据不平衡对于强大的基因组选择至关重要.