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相关概念视频

Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Probability Laws01:49

Probability Laws

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Overview
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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Probability in Statistics01:14

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Distributions01:32

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Updated: Jun 29, 2025

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

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一个概率知识图表用于目标识别.

Chang Liu1, Kaimin Xiao2,3, Cuinan Yu4

  • 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.

PLoS computational biology
|April 5, 2024
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概括
此摘要是机器生成的。

新的机器学习框架Progeni通过整合生物网络和文献数据来识别有效的药物目标. 这种方法加速了药物发现,并已在癌症点的湿实验室实验中得到验证.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 机器学习在药物发现中的作用
  • 生物信息学是一种生物信息学.

背景情况:

  • 药物发现是昂贵的,而且容易失败.
  • 实验性目标识别方法是劳动密集型的.
  • 计算方法,特别是机器学习,对药物发现有希望.

研究的目的:

  • 引入Progeni,一个新的机器学习框架,用于识别安全有效的疾病点.
  • 提高药物发现的效率和成功率.

主要方法:

  • 普罗格尼集成了来自不同来源的异质生物网络.
  • 它通过结合文献证据来构建一个概率知识图.
  • 图形神经网络学习生物实体识别的特征嵌入.

主要成果:

  • 与基线方法相比,Progeni表现出优异的预测性能.
  • 该框架显示了对暴露偏差的稳定性.
  • 普罗格尼确定了新的目标,并得到了现有文献的强烈支持.
  • 湿实验室实验验证实了黑色素瘤和结直肠癌预测目标的生物学意义.

结论:

  • 普罗格尼是推动药物发现的强大工具.
  • 它有效地识别了生物相关和验证的药物标.
  • 该框架为传统方法提供了更有效和可靠的替代方案.