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相关概念视频

Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Attribution01:26

Attribution

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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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Self-Concept01:19

Self-Concept

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Self-concept is the cognitive and emotional understanding individuals hold about their identity. It evolves through various developmental stages, beginning in infancy and maturing as children grow. This concept influences how individuals perceive their abilities, interact with others, and manage challenges throughout life.
Infancy and Emerging Recognition
During infancy, self-concept is virtually nonexistent. Babies do not distinguish themselves as separate entities and often mistake their...
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Genomics02:02

Genomics

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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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Personal Choice and Fate Attributions01:19

Personal Choice and Fate Attributions

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Some individuals interpret life events as a consequence of their personal choices and actions, while others believe that outcomes are dictated by fate or destiny. This divergence in perspective has been examined in psychological and cross-cultural studies, particularly in relation to religious faith and cultural beliefs about causality.Fate and Personal ResponsibilityPeople who emphasize personal responsibility view events as direct consequences of their decisions. For instance, breaking a leg...
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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TPCAV:通过概念归因解释深度学习基因组学模型.

Jianyu Yang, Shaun Mahony

    bioRxiv : the preprint server for biology
    |February 9, 2026
    PubMed
    概括

    解释基因组学深度学习模型现在更容易使用TPCAV. 这种新方法增强了DNA图案的概念归属性,染色质可访问性等,提供可靠的基因组特征解释.

    科学领域:

    • 基因组学就是基因组学.
    • 计算生物学 计算生物学
    • 机器学习 机器学习

    背景情况:

    • 在基因组学中解释深度学习模型是困难的.
    • 目前的方法与复杂的基因组特征 (如染色质可访问性) 相斗争.
    • 概念归因方法提供全球解释,但尚未在基因组学中广泛使用.

    研究的目的:

    • 引入第一个概念归因的应用,用于解释基因组学深度学习模型.
    • 开发一种改进的方法,TPCAV (用PCA设计的概念激活矢量进行测试),解决现有技术的局限性.
    • 为了能够对各种基因组特征和模型类型进行可靠的解释.

    主要方法:

    • 调整了使用概念激活矢量 (TCAV) 方法进行测试的方法.
    • 引入了使用PCA基于基因组嵌入特征的脱相关性TPCAV.
    • 开发了一种用于提取特定概念输入归因图的策略.
    • 在各种基因组学模型和输入表示上对TPCAV进行了评估.

    主要成果:

    • 与TCAV相比,TPCAV提供了比TCAV更可靠的DNA图案解释.
    • 对于DNA模式预测,TPCAV的性能与TF-MoDISco相美.

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  • TPCAV成功地解释了诸如重复元素和色素可访问性之类的一般概念.
  • TPCAV可以概括为基础模型和具有染色体信号输入的模型.
  • TPCAV识别了与特定概念相关的代表性转录因子结合部位.
  • 结论:

    • TPCAV提供了一种灵活而强大的方法来解释基因组学深度学习模型.
    • 这种方法超越了DNA动机分析,扩展到更广泛的基因组特征和先进的模型架构.
    • TPCAV有助于更深入的生物学见解和下游调查不同的约束机制.