Search research articles

ACERCA DE JoVE

Visión General Liderazgo Blog Centro de Ayuda JoVE

AUTORES

Proceso de Publicación Consejo Editorial Alcance y Políticas Revisión por Pares Preguntas Frecuentes Enviar

BIBLIOTECARIOS

Testimonios Suscripciones Acceso Recursos Consejo Asesor de Bibliotecas Preguntas Frecuentes

INVESTIGACIÓN

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archivo

EDUCACIÓN

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Centro de Recursos para Profesores Sitio de Profesores

Términos y Condiciones de Uso

Política de Privacidad

Videos de Conceptos Relacionados

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

Modern Molecular Taxonomy

Modern Molecular Taxonomy

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...

Prediction Intervals

Prediction Intervals

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.

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por

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

Ver todos los artículos relacionados

Search research articles

Video Experimental Relacionado

Updated: Jan 7, 2026

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

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

Published on: July 24, 2019

Un marco de aprendizaje automático consciente del tiempo permite la predicción de la dinámica de comunidades

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.

|December 30, 2025

Resumen

Este resumen es generado por máquina.

MicroProphet predice la dinámica de la comunidad microbiana a partir de datos escasos sin imputación. Este marco personalizado y consciente del tiempo permite la detección temprana de enfermedades y la inferencia de líneas de tiempo forenses.

Palabras clave:

dinámica de comunidades microbianas aprendizaje automático medicina de precisión microbioma Transformer

Más Videos Relacionados

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

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

Published on: April 6, 2021

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

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

Published on: July 12, 2018

Videos de Experimentos Relacionados

Last Updated: Jan 7, 2026

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

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

Published on: July 24, 2019

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

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

Published on: April 6, 2021

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

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

Published on: July 12, 2018

Área de la Ciencia:

Ecología Microbiana; Biología Computacional; Medicina de Precisión

Sus antecedentes:

La predicción de la dinámica de las comunidades microbianas a partir de datos longitudinales escasos es un desafío para la medicina de precisión y el monitoreo ecológico.
Los modelos existentes a menudo dependen de la imputación de datos y asumen dinámicas a nivel de población, lo que limita las predicciones personalizadas.

Objetivo del estudio:

Desarrollar un marco personalizado y consciente del tiempo para la predicción precisa de la abundancia microbiana a partir de datos longitudinales incompletos.
Permitir predicciones precisas sin necesidad de imputación de datos.

Principales métodos:

Se propuso MicroProphet, un marco que utiliza una arquitectura Transformer consciente del tiempo.
Se reconstruyeron trayectorias microbianas específicas del sujeto utilizando solo el 30% inicial de los puntos de tiempo observados.
Se empleó un mecanismo de atención para capturar estados de transición críticos.

Principales resultados:

Demostró una generalización robusta entre ecosistemas en comunidades sintéticas, microbiomas intestinales humanos, desarrollo intestinal infantil y descomposición de cadáveres.
Logró una alta precisión predictiva e interpretabilidad biológica.
Permitió la detección temprana de cambios microbianos asociados con enfermedades y optimizó el momento de las intervenciones del microbioma.
Infirió con precisión las líneas de tiempo de descomposición en entornos forenses.

Conclusiones:

MicroProphet transforma datos incompletos del microbioma en pronósticos procesables e individualizados.
Sienta las bases para sistemas conscientes del tiempo en ecología microbiana y salud de precisión.