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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

494
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
494
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

126
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
126
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

710
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
710
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.5K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.5K

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

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset.

bioRxiv : the preprint server for biology·2026
Same author

Statistical inference for high-dimensional generalized estimating equations.

Biostatistics (Oxford, England)·2026
Same author

Large-Scale Proteomic Profiling of Incident Heart Failure and Its Subtypes in Older Adults.

Circulation. Genomic and precision medicine·2026
Same author

Integrative Multiomics for Prognostic Assessment in Pulmonary Arterial Hypertension.

Circulation. Heart failure·2025
Same author

Transcriptome-wide association studies at cell state level using single-cell eQTL data.

medRxiv : the preprint server for health sciences·2025
Same author

Proteomic Signatures of Right Ventricular Outcomes in Pulmonary Arterial Hypertension.

Circulation. Heart failure·2024
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
関連記事をすべて見る

関連する実験動画

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

2.3K

特定できないガウスモデルから指向アサイクルグラフを学習するための整数プログラミング

Tong Xu1, Armeen Taeb2, Simge Küçükyavuz1

  • 1Department of Industrial Engineering and Management Sciences, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA.

Biometrika
|August 25, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,連続データから指向アサイクルグラフ (DAG) を学習するための新しい方法を導入し,さまざまなノイズレベルを処理し,最適なソリューションを確保することで,既存の技術の限界を克服します.

キーワード:
ベイジアンネットワーク特定可能性混合整数プログラミング構造方程式モデル

さらに関連する動画

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

492

関連する実験動画

Last 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

2.3K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

492

科学分野:

  • 機械学習
  • 原因推論
  • グラフ理論

背景:

  • 観測データから指向アサイクルグラフ (DAG) を学ぶことは,因果推論にとって極めて重要です.
  • 現在の方法はしばしば最適性の保証がないか,ホモスケダスティックノイズを想定し,その適用性を制限する.
  • これらの制限は,正確なモデル識別を妨げ,不適切な構造の学習につながる可能性があります.

研究 の 目的:

  • 継続的な観測データから DAG を学習するための堅牢で計算効率の良いフレームワークを開発する.
  • 特に最適化保証と騒音の仮定に関して,既存の方法の欠陥を解決する.
  • 任意のヘテロスケダスティックノイズを考慮する方法を提供します.

主な方法:

  • DAGの学習のための混合整数プログラミングフレームワークが開発されました.
  • この方法は,任意のヘテロスケダスティックノイズを含み,ホモスケダスティック仮定よりも有意な改善です.
  • アシンプトティックに最適な解決策を達成するために,ブランチ・アンド・バインド手順の早期停止基準が導入されました.

主要な成果:

  • 提案されたフレームワークは,数値実験における最先端のアルゴリズムと比較して優れたパフォーマンスを示しています.
  • この方法は,性能が低下する競合するアプローチとは異なり,騒音ヘテロスケダスティシティに強固です.
  • 早期停止基準で得られた近似溶液の一貫性が確立される.

結論:

  • 開発された混合整数プログラミングフレームワークは,連続データからDAGを学習するための効率的で正確なアプローチを提供します.
  • この方法は,既存の技術の主要な限界を克服し,最適性を保証し,複雑なノイズ構造を処理します.
  • micodag Python パッケージの利用は,この高度な構造学習技術の適用を容易にする.