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相关概念视频

Reason and Intuition01:37

Reason and Intuition

7.5K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.5K
Reasoning01:30

Reasoning

439
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
439
Deductive Reasoning01:16

Deductive Reasoning

69.0K
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.
For example, a researcher can deduce specific predictions...
69.0K
Retrieval01:12

Retrieval

447
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
447
Inductive Reasoning00:59

Inductive Reasoning

67.8K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
67.8K
Clinical Applications of Epidermal Stem Cells01:19

Clinical Applications of Epidermal Stem Cells

3.3K
Epidermal stem cells (EpiSCs) are mainly located at the basal layer of the epidermis. These cells repair minor injuries of the skin and replace dead skin cells. However, EpiSCs’ cannot heal severe wounds such as major burns or those from diabetes or hereditary disorders. In such cases, culturing the epidermal stem cells from the patient is possible and has yielded successful treatment options, such as laboratory-grown skin grafts. These grafts are synthesized using a patient’s own...
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相关实验视频

Updated: Feb 7, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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为可解释推理进行生物医学知识图表检索的缩放:应用于临床诊断预测的应用.

He Cheng, Yifu Wu, Saksham Khatwani

    medRxiv : the preprint server for health sciences
    |February 6, 2026
    PubMed
    概括

    在大型生物医学知识图 (KG) 上,LogosKG提供高效的多跳转检索. 这种硬件优化的系统通过实现可解释的,深度的图形穿越来改善诊断推理.

    科学领域:

    • 生物医学信息学 生物医学信息学
    • 图形数据库 图形数据库
    • 计算生物学 计算生物学

    背景情况:

    • 生物医学知识图 (KG) 对于组织复杂的生物和临床数据至关重要.
    • 从大型KG中进行可扩展和可解释的多跳检索是一个重要的计算挑战.
    • 目前的系统在与先进的生物医学推理所需的规模和深度作斗争.

    研究的目的:

    • 开发一个硬件优化的系统,LogosKG,在大型生物医学KG上进行高效的k-hop检索.
    • 为了解决访问深度,多跳图结构的计算瓶.
    • 通过改进KG穿越来增强诊断推理.

    主要方法:

    • 逻辑KG使用符号图表配方和硬件高效执行 k-hop 穿越.
    • 关键技术包括程度意识的分区,跨分区路由和按需缓存.
    • 该系统旨在扩展到数十亿边缘图形,同时保持检索准确度.

    主要成果:

    • 与基于CPU和GPU的方法相比,LogosKG显示了相当大的效率提升.
    • 该系统成功地扩展到非常大的生物医学图 (数十亿边缘尺度).
    • 实验表明,检索保真度和性能得到了改善.

    更多相关视频

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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    Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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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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    Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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    Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

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    结论:

    • 洛格斯KG提供了一个可扩展和高效的解决方案,用于深度生物医学KG的多跳转检索.
    • 通过LogosKG访问深度图形结构可以促进可解释的诊断证据的传播.
    • 评估表明使用高跳式KG检索提高了诊断推理质量 (准确性,可理解性,简洁性).