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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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ER Retrieval Pathway01:45

ER Retrieval Pathway

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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
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Transducer Mechanism: Enzyme-Linked Receptors01:27

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Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
Major types that are helpful drug targets include:
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¹H NMR Signal Integration: Overview00:58

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The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
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Genomics02:02

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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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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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相关实验视频

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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多维信息引导的提及集成和多维推理用于生物医学文档级关系提取.

Xinyu He1, Yuning Zhang2, Yonggong Ren2

  • 1School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, Liaoning, China; Information and Communication Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China; Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China.

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概括

这项研究引入了用于生物医学文档级关系提取的新方法,改善了在文本中的复杂关系的识别. 这种方法增强了实体表示,并推断出隐含的连接,以便更好地重建路径.

关键词:
生物医学文档层面的关系提取化学-疾病关系提取提取.提及整合的整合.推理 推理 推理 推理这就是U-Net.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 计算生物学 计算生物学

背景情况:

  • 生物医学文档级别的关系提取是具有挑战性的,因为多句子的实体提及.
  • 现有的方法往往忽略了提及级别的细节,并使用简单的聚合来表示实体.
  • 不相关的提及和杂的数据使准确的关系识别变得复杂.

研究的目的:

  • 开发一种强大的生物医学文档级关系提取方法.
  • 通过有效整合提及级信息来改善实体表示.
  • 在生物医学文本中推断隐含和复杂的关系.

主要方法:

  • 引入了多维信息导向提及集成模块,包括提及图表和上下文意识的注意力.
  • 开发了一个使用逻辑模式和U-Net网络推断隐含关系的多维推理模块.
  • 专注于过不相关的提及和捕获上下文敏感信息.

主要成果:

  • 在CDR数据集上获得了86.7%的高F分,在GDA数据集上达到84.7%.
  • 通过过杂的提及,证明了强大的实体表示.
  • 成功重建复杂的生物医学途径和实体相互作用.

结论:

  • 拟议的方法显著增强了生物医学文档级关系提取.
  • 有效地整合提及级信息和多维推理,提高了准确性.
  • 该方法在生物医学和一般域数据集上表现出强的表现.