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相关概念视频

Protein and Protein Structure02:15

Protein and Protein Structure

87.9K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
87.9K
Structural Protein Function01:56

Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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Structural Protein Function01:56

Structural Protein Function

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Protein and Protein Structures02:15

Protein and Protein Structures

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Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Collagens are the Major Structural Proteins of ECM01:13

Collagens are the Major Structural Proteins of ECM

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Three main types of fibers are secreted by fibroblasts: collagen fibers, elastic fibers, and reticular fibers. Collagen fiber is made from fibrous protein subunits linked together to form a long, straight fiber. Collagen fibers, while flexible, have great tensile strength, resist stretching, and give ligaments and tendons their characteristic resilience and strength. These fibers hold connective tissues together, even during the body's movement.
Connective tissue proper includes loose...
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相关实验视频

Updated: Feb 5, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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超越精选知识:结构性蛋白质嵌入增强基于GNN的个性化癌症预后

Sofia Ormazabal Arriagada, Tsung-Wei Lin, Marta Misztal

    IEEE journal of biomedical and health informatics
    |February 3, 2026
    PubMed
    概括

    我们开发了GLLM,这是一个新的图形学习框架,集成了基因表达,临床数据和蛋白质结构,以预测5年癌症风险. 这种方法可以提高预后准确性,以实现个性化的患者监测和治疗优先级.

    科学领域:

    • 在瘤学瘤学.
    • 生物信息学是一种生物信息学.
    • 机器学习 机器学习

    背景情况:

    • 准确的癌症预后模型对于治疗规划和资源分配至关重要.
    • 当前的模型往往缺乏整合多omics数据和结构蛋白质信息.
    • 根据风险对患者进行分层是个性化后续计划的必要条件.

    研究的目的:

    • 引入GLLM,用于癌症患者风险分层的多模式图形学习框架.
    • 整合RNA-seq配置文件,临床变量和蛋白质结构嵌入,以提高预后准确度.
    • 与现有方法相比,评估GLLM在多种癌症类型中的性能.

    主要方法:

    • GLLM使用图形神经网络,在蛋白质-蛋白质相互作用图中将基因作为节点.
    • 一种新的融合机制,SCANE,将患者特异性的基因表达与结构性蛋白质嵌入相结合.
    • 该框架处理RNA序列数据,临床变量和蛋白质结构信息.

    主要成果:

    • 在乳腺癌,肺癌和结直肠癌队列中,GLLM在精度回忆曲线下的区域有所改善.
    • 该模型在风险预测方面表现优于强大的临床和分子基线.
    • 序列衍生结构嵌入被证明优于基于文本的生物医学嵌入.

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    结论:

    • GLLM提供了一种有效的融合策略,用于通过基因表达和蛋白质结构来增强节点表示.
    • 该框架通过识别高风险癌症患者来支持个性化监测规划.
    • GLLM的轻量级架构允许无集成到临床瘤学工作流程中.