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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
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Most plants use the C3 pathway for carbon fixation. However, some plants, such as sugar cane, corn, and cacti that grow in hot conditions, use alternative pathways to fix carbon and conserve energy loss due to photorespiration. Photorespiration is the process that occurs when the oxygen concentration is high. Under such conditions, the rubisco enzyme in the Calvin cycle binds O2 instead of CO2, which halts photosynthesis and consumes energy.
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The gene encoding the main signaling molecules of the Wnt signaling pathways (the Wnt proteins) was discovered almost four decades ago by Nüsslein-Volhard and Wieschaus. They identified and originally named the gene "wingless" (wg) after a phenotype discovered during their landmark genetic screen in Drosophila for body pattern defects. At around the same time, another researcher named Harold Varmus found that a murine tumor virus activates the mammalian wg homolog, Int-1, which...
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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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相关实验视频

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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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产品多重表示用于学习生物途径.

Daniel McNeela1,2,3, Frederic Sala1, Anthony Gitter1,2,3

  • 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.

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概括

混合曲率嵌入通过减少扭曲和提高分布内预测来改善生物通路图分析. 然而,这些图形表示学习模型可能过度适应,在分布之外的任务中表现不佳.

科学领域:

  • 计算生物学 计算生物学
  • 图形表示学习学习学习图形表示学习
  • 网络科学 网络科学

背景情况:

  • 在非欧几里德空间中嵌入图形的机器学习模型提供了优势,但在生物通路分析中尚未得到充分探索.
  • 生物通路图具有复杂的结构,挑战现有的嵌入方法.
  • 高质量的嵌入对于理解疾病机制和构建预测模型至关重要.

研究的目的:

  • 研究嵌入生物路径图形在非欧几里德混合曲率空间的影响.
  • 将混合曲率嵌入与传统的欧几里德图表达式学习进行比较.
  • 评估学习嵌入的性能,以预测缺失的蛋白质-蛋白质相互作用.

主要方法:

  • 使用非欧几里得曲线空间嵌入生物路径图.
  • 使用的混合曲率产品图卷积网络 (GCNs).
  • 在学习节点嵌入上训练了一个受监督的模型,用于边缘预测.

主要成果:

  • 通过混合曲率嵌入实现了显著的扭曲降低.
  • 观察到混合曲率GCN的分布边缘预测性能有所改善.
  • 与基线相比,在分布外边缘预测方面表现不佳,这表明潜在的过度装配.

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结论:

  • 混合曲率嵌入显示出分析生物通路图的前景,特别是用于分布内任务.
  • 在混合曲率表示中超拟合的可能性需要进一步调查,以便进行可靠的生物网络分析.
  • 混合曲率GCN的代码和路径分析是公开的.