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相关概念视频

Homologous Recombination02:31

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Transposons make up a significant part of genomes of various organisms. Therefore, it is believed that transposition played a major evolutionary role in speciation by changing genome sizes and modifying gene expression patterns. For example, in bacteria, transposition can lead to conferring antibiotic resistance. Movement of transposable elements within the genetic pool of pathogenic bacteria can aid in transfer of antibiotic-resistant genetic elements. In eukaryotes, transposons can carry out...
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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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通过强化学习构建祖先重组图.

Mélanie Raymond1, Marie-Hélène Descary1, Cédric Beaulac1

  • 1Department of Mathematics, Université du Québec à Montréal, Montréal, QC, Canada.

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概括
此摘要是机器生成的。

这项研究引入了强化学习 (RL) 来构建祖先重组图 (ARG),以建模遗传关系. RL方法以最小的重组事件构建精确的ARG,为传统方法提供数据驱动的替代方案.

关键词:
祖先的重组图表可以显示出祖先重组图.集合方法 集合方法 集合方法基因谱系 基因谱系 基因谱系遗传统计学遗传统计学神经网络的神经网络的神经网络强化学习是一种强化学习.

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科学领域:

  • 计算生物学 计算生物学
  • 遗传学 是一个遗传学.
  • 机器学习 机器学习

背景情况:

  • 祖先重组图 (ARG) 对于理解遗传关系至关重要.
  • 现有的ARG构造方法通常假定最有可能的图形具有最少的重组事件.
  • 这些方法经常依赖于启发式规则或复杂的理论模型.

研究的目的:

  • 引入一种新的方法来构建最大限度的节祖先重组图 (ARG).
  • 利用强化学习 (RL) 来直接从遗传数据中构建ARG.
  • 为了比较基于RL的ARG构建与现有启发式算法的性能.

主要方法:

  • 该研究将ARG构建作为最短路径问题,类似于强化学习中的迷宫导航任务.
  • 一个RL代理学习最佳行动 (凝聚,突变,重组) 以有效地到达最近的共同祖先.
  • 这种方法利用了在迷宫中找到最短的路径和确定最节的ARG之间的相似之处.

主要成果:

  • 强化学习 (RL) 成功构建了 ARG,其重组事件数量与优化启发式算法相比,有时也比其少.
  • 对于给定的遗传样本,RL方法产生了节的ARG分布.
  • 该方法展示了概括能力,在培训期间未遇到的新数据集上表现良好.

结论:

  • 强化学习为构建祖先重组图表提供了一种有希望和创新的方法.
  • 这种数据驱动的方法绕过了预定义的启发式规则或复杂的理论框架的需求.
  • RL提供了一种灵活而有效的替代方案,用于推断ARG所代表的复杂遗传关系.