Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
What are Populations and Communities?00:30

What are Populations and Communities?

34.8K
Overview
34.8K
What is Population Genetics?01:25

What is Population Genetics?

59.5K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
59.5K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

382
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
382
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

59.4K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
59.4K
Conservation of Declining Populations02:07

Conservation of Declining Populations

9.7K
Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
9.7K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

BioData mining·2025
Same author

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics·2025
Same author

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.

Patterns (New York, N.Y.)·2025
Same author

AI as an accelerator for defining new problems that transcends boundaries.

BioData mining·2025
Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025
Same author

ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning.

Bioinformatics (Oxford, England)·2025

関連する実験動画

Updated: Sep 9, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.9K

複雑でダイナミックな人口構造:統合,未解決問題,将来の方向

Joshua L Payne1, Mario Giacobini2, Jason H Moore1

  • 1Computational Genetics Laboratory, Dartmouth Medical School, 1 Medical Center Drive, Lebanon, NH, USA.

Soft computing
|August 29, 2025
PubMed
まとめ

進化アルゴリズムの集団構造は 検索パフォーマンスに影響します このレビューは,複雑な静的な構造を合成し,将来の最適化研究のために,アクティブリンクのようなダイナミックな構造を探索します.

キーワード:
アソルタティビティ進化的アルゴリズム相互作用トポロジーネットワークスケールフリー小さい世界

さらに関連する動画

Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

6.3K
Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria
05:44

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Published on: December 27, 2024

997

関連する実験動画

Last Updated: Sep 9, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.9K
Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

6.3K
Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria
05:44

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Published on: December 27, 2024

997

科学分野:

  • コンピュータ科学
  • 人工知能
  • 計算による最適化

背景:

  • 集団の構造は,進化アルゴリズムにおけるアレル拡散と混合に不可欠であり,検索の性能に直接影響する.
  • 最近の研究は,異質な接続性,クラスタリング,および程度の相関性を持つ複雑な静的な集団構造に焦点を当てています.

研究 の 目的:

  • 進化的アルゴリズムにおける複雑な静的な集団構造に関する最近の発見を合成する.
  • 複雑な静的構造の限界を特定し,理論的/実用的な問いを開きます.
  • 研究不足の動的集団構造とその進化的最適化の可能性を探求する.

主な方法:

  • 複雑な静的な人口構造に関する既存の研究の文献合成.
  • 現在のアプローチの理論的・実用的な限界を議論する.
  • "アクティブリンク"メカニズムを含むダイナミックな人口構造の探索.

主要な成果:

  • 複雑な静的な構造は潜在的ですが,未解決の理論的な問題と未証明の実用的な有用性があります.
  • ダイナミックな集団構造,特に"アクティブリンク"は,進化の最適化のための有望な,しかしほとんど未開拓の道を示しています.

結論:

  • 理論上の欠陥を解決し,複雑な静的構造の実用的な利点を示すために,さらなる研究が必要である.
  • ダイナミックな集団構造,特に適応的な再配線 ("アクティブ・リンク) を含むものは,進化的探求の強化のための重要な将来の調査を保証する.