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Probability Distributions01:32

Probability Distributions

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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.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Probability Histograms01:17

Probability Histograms

12.2K
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.
12.2K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Random Sampling Method01:09

Random Sampling Method

12.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Probability Laws01:49

Probability Laws

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Overview
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Updated: Sep 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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確率ネットワークから派生したグラフの周波数境界を指定したランダムグラフを生成する

Bram Mornie1, Didier Colle1, Pieter Audenaert1

  • 1IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.

PloS one
|August 26, 2025
PubMed
まとめ

この研究は,サブグラフパターンとエッジの不確実性を制御して,現実的な生物学的ネットワークを生成するための新しいアルゴリズムを導入します. この方法は,特定のモチーフ周波数を持つ大きなグラフを効率的に作成し,正確なバイオインフォマティクスアルゴリズムテストに不可欠です.

科学分野:

  • バイオ情報学
  • コンピュータ生物学
  • ネットワーク科学

背景:

  • バイオインフォマティクスアルゴリズムのテストには現実的なネットワークモデルが必要です.
  • 既存のグラフ生成方法は,サブグラフパターン (グラフレット) とエッジの不確実性をしばしば無視しています.
  • 生物学的相互作用の確率モデル化は不可欠ですが,しばしば無視されます.

研究 の 目的:

  • 生物情報学の新しいランダムグラフ生成アルゴリズムを開発する.
  • 合成ネットワークにおけるグラフレット周波数と度分布の制御を組み込む.
  • 生物学的ネットワークのエッジの不確実性をモデル化するという課題に取り組むこと.

主な方法:

  • 確率ネットワークのグラフレット数値と度数分布を導いた.
  • 効率的なグラフレットカウントを持つインクリメンタルグラフ生成アルゴリズムを開発した.
  • アルゴリズムの更新は,ノード数に関係なく,稀なグラフで効率的にカウントします.

主要な成果:

  • 3ノードと4ノードグラフレットの制御周波数で合成と実際のネットワークを生成します.
  • 効率的なグラフ生成を 1 時間以内に 10,000 以上のエッジで実証しました.
  • 不確実性の度合いを把握する アルゴリズムの能力を示しました

結論:

  • この新しいアルゴリズムは より現実的で正確な 合成生物学的ネットワークの作成を可能にします
  • このアプローチは,バイオ情報学のネットワーク分析とアルゴリズムベンチマークの信頼性を向上させます.
  • 効率的なグラフレット制御と不確実性モデリングは,生物学的ネットワーク生成の重要な進歩です.

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