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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

462
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
462
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

221
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...
221
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

536
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
536
Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K

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

Transfer learning identifies bacterial signatures for cross-regional diagnosis of type 2 diabetes and enable stage-sensitive dietary fiber intervention.

iMetaOmics·2026
Same author

MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

CXCR6 as a novel therapeutic target in allergic asthma.

International immunopharmacology·2025
Same author

Potential biases and limitations in inferring the historic range of Yangtze finless porpoises from classic poems.

Current biology : CB·2025
Same author

Intra-tumor microbiome-based tumor survival indices predict immune interaction and drug sensitivity on pan-cancer scale.

mSystems·2025
Same author

Microbial resources and interactions across three-dimensional space for a freshwater ecosystem.

The Science of the total environment·2025
Same journal

Conserved 3' stem-loop structures enable comprehensive analysis of bacterial transcription termination in metagenomes.

Microbiome·2026
Same journal

Correction: Heat stress suppresses lactation through potential rumen-mammary communication mediated by extracellular vesicles: integrated analysis of microbiome, metabolome, and miRNA profiles.

Microbiome·2026
Same journal

Museomics reveals uncultured symbionts with biosynthetic potential in nudibranchs.

Microbiome·2026
Same journal

Population-based characterisation of child and adolescent oral bacterial microbiomes.

Microbiome·2026
Same journal

Uncovering transcriptional processes in microbial communities adapted to differing saline conditions in salt-weathered historic buildings.

Microbiome·2026
Same journal

IL-17A deficiency in HLA-DR3 transgenic mice enriches beneficial Prevotella species in gut to promote Tregs and reduce CNS autoimmunity.

Microbiome·2026
関連記事をすべて見る

関連する実験動画

Updated: Jan 7, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K

個々の精度で微生物群集ダイナミクス予測を可能にする時間認識型機械学習フレームワーク

Yuli Zhang1, Kouyi Zhou1, Xiaoke Chen1

  • 1Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.

Microbiome
|December 30, 2025
PubMed
まとめ
この要約は機械生成です。

MicroProphetは、補完なしで疎なデータから微生物群集ダイナミクスを予測します。この個別化された時間認識型フレームワークは、早期の疾患検出と法医学的タイムライン推論を可能にします。

キーワード:
微生物群集ダイナミクス機械学習精密医療時間認識型モデリングMicroProphet

さらに関連する動画

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.9K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.4K

関連する実験動画

Last Updated: Jan 7, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.9K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.4K

科学分野:

  • 微生物生態学
  • 計算生物学
  • 精密医療

背景:

  • 疎な縦断データからの微生物群集ダイナミクスの予測は、精密医療および生態学的監視にとって困難です。
  • 既存のモデルは、データ補完に依存し、集団レベルのダイナミクスを仮定することが多く、個別化された予測を制限しています。

研究 の 目的:

  • 不完全な縦断データからの正確な微生物存在量予測のための、個別化された時間認識型フレームワークを開発すること。
  • データ補完の必要なしに正確な予測を可能にすること。

主な方法:

  • 時間認識型Transformerアーキテクチャを利用するフレームワークであるMicroProphetを提案しました。
  • 観測されたタイムポイントの最初の30％のみを使用して、被験者固有の微生物軌道を再構築しました。
  • 重要な遷移状態を捉えるために注意メカニズムを採用しました。

主要な成果:

  • 合成コミュニティ、ヒトの腸内微生物叢、乳児の腸内発達、死体分解にわたる堅牢なクロスエコシステム一般化可能性を実証しました。
  • 高い予測精度と生物学的解釈可能性を達成しました。
  • 疾患関連微生物シフトの早期検出と微生物叢介入の最適なタイミングを可能にしました。
  • 法医学の設定における分解タイムラインを正確に推測しました。

結論:

  • MicroProphetは、不完全なマイクロバイオームデータを、実行可能で個別化された予測に変換します。
  • 微生物生態学および精密医療における時間認識型システムの基礎を築きます。