Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

99
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
99
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

137
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...
137
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.

Briefings in bioinformatics·2026
Same author

Green Tea Catechin Plus Inulin Improves Insulin Resistance Without Reducing Visceral Fat and Shows Exploratory Gut Microbiota Signals in Adults with Visceral Obesity: A Double-Blind Randomized Controlled Trial.

Nutrients·2026
Same author

Patch-level phenotype identification via weakly supervised neuron selection in sparse autoencoders for CLIP-derived pathology embeddings.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

Using Machine Learning to Identify Factors Affecting Antibody Production and Adverse Reactions After COVID-19 Vaccination.

Vaccines·2026
Same author

Multihit Somatic Mosaicism of <i>TP53</i> Pathogenic Variants in a Patient Mimicking Li-Fraumeni Syndrome.

JCO precision oncology·2025
Same author

Intricate interactions between fine-scale genetic structure, lifestyle, and dietary habits in the Japanese population.

Communications biology·2025

Related Experiment Video

Updated: Sep 10, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

29.4K

Diffusion model for imputing time-series gut microbiome profiles using phylogenetic information and metadata

Misato Seki1, Yao-Zhong Zhang1, Seiya Imoto1

  • 1Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.

Bioinformatics Advances
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel imputation framework for time-series microbiome data with missing values. The diffusion model effectively handles missing data, improving downstream predictive tasks for gut microbial community analysis.

More Related Videos

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.0K
Characterizing Microbiome Dynamics &#8211; 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

12.1K

Related Experiment Videos

Last Updated: Sep 10, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

29.4K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.0K
Characterizing Microbiome Dynamics &#8211; 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

12.1K

Area of Science:

  • Microbiome research
  • Genomic data analysis
  • Computational biology

Background:

  • The gut microbiota is vital for host health, and analyzing its dynamic changes requires time-series genomic data.
  • Missing values in these datasets pose significant challenges for accurate analysis.
  • Understanding microbial community dynamics is crucial for health and disease research.

Purpose of the Study:

  • To develop an effective imputation framework for time-series microbiome data with missing values.
  • To leverage diffusion models for handling missing genomic data in microbiome studies.
  • To improve the accuracy of downstream predictive tasks using imputed microbiome data.

Main Methods:

  • A conditional score-based diffusion model was developed, incorporating phylogenetic convolutional layers tailored for microbiome data.
  • The framework was evaluated on both 16S rRNA and whole-genome shotgun sequencing data across various missing data ratios.
  • Host metadata was integrated into the model using a tabular encoding approach to enhance imputation performance.

Main Results:

  • The proposed method significantly reduced mean absolute error in imputation across different missing data levels.
  • Imputed datasets improved the performance of downstream predictive tasks, achieving competitive or superior area under the curve scores compared to existing methods.
  • Integrating host metadata further boosted imputation accuracy, especially when dealing with higher proportions of missing data.

Conclusions:

  • The diffusion model offers a powerful approach for imputing missing values in time-series microbiome data.
  • This framework enhances the reliability of microbiome data analysis, particularly in longitudinal studies.
  • The developed method provides a valuable tool for researchers studying the complex interactions within the gut microbiome.