Gut microbiome-metabolome interactions predict host condition.

Oshrit Shtossel, Omry Koren, Iris Shai, Ehud Rinott, Yoram Louzoun

February 2024 Microbiome

Synopsis of Social media discussions

Various discussions highlight key aspects of the publication, such as highlighting LOCATE as an innovative machine learning tool or emphasizing how understanding microbiome-metabolome interactions can improve predictions about host health. The tone ranges from informative to enthusiastic, with phrases like 'promising approach' and 'better predictions,' which contribute to a generally positive perception of the article’s significance and potential impact.

A
Agreement
 Moderate agreement

Many posts express support or positive acknowledgment of the study, such as calling it a 'promising approach' or recognizing 'the importance of microbiome-metabolome interactions,' indicating general agreement with the significance of the findings.

I
Interest
 High level of interest

Posts show high interest through enthusiasm and curiosity, especially when mentioning the use of machine learning tools like LOCATE or emphasizing the potential for predicting host conditions, suggesting they find the research compelling.

E
Engagement
 Moderate level of engagement

While some contributors reference the technical aspects or implications of the study, the engagement appears moderate, with casual mentions and basic summaries rather than deep critique or analysis.

I
Impact
 Moderate level of impact

The discussions reflect a perception that this research could influence future studies or clinical applications, but this is often implied rather than explicitly argued; hence, a moderate perceived impact score.

Social Mentions

YouTube

2 Videos

Twitter

27 Posts

Blogs

2 Articles

Metrics

Video Views

188

Total Likes

85

Extended Reach

109,436

Social Features

31

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

LOCATE: A Machine Learning Tool for Microbiome and Metabolome Interaction Prediction

LOCATE: A Machine Learning Tool for Microbiome and Metabolome Interaction Prediction

Shtossel et al. introduce LOCATE, a machine learning model that predicts host health by modeling complex microbiome-metabolome interactions, offering improved accuracy and insights into host conditions.

AJE Video Bytes

February 12, 2024

183 views

Advances in Gut Microbiome-Metabolome Interaction Modeling for Disease Prediction

Advances in Gut Microbiome-Metabolome Interaction Modeling for Disease Prediction

Shtossel O Koren O Shai I Rinott E Louzoun Y 2024 Gut microbiomemetabolome interactions predict host conditions. The study introduces LOCATE, a machine learning tool that models the microbiome-metabolome interaction as a complex equilibrium, improving predictions of host health.

Tiffany Carrillo

March 11, 2025

5 views

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    An article in @MicrobiomeJ presents LOCATE: a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation to predict the host condition and disease states. https://t.co/0cQC4EXmr3
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    February 27, 2024

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  • Rostand Chamedjeu
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  • Lei ZHANG
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    RT @MicrobiomeJ: Gut microbiome-metabolome interactions predict host condition https://t.co/22O78NnAxu https://t.co/roAr1NXfik
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  • Sunjae Lee
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    February 11, 2024

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  • Yu Nakajima
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    February 11, 2024

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  • Prasath Lab CCHMC
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  • abdul Jabbar
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    February 11, 2024

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Abstract Synopsis

  • The study introduces LOCATE, a machine learning tool that models the microbiome-metabolome interaction as an equilibrium of complex relationships, improving the prediction of metabolite concentrations and host conditions compared to previous methods.
  • LOCATE's latent representation captures the interaction between microbiome composition and metabolite levels, providing better predictions of host health and demographics, even from limited data.
  • The research highlights that understanding the microbiome-metabolome interaction as a combined, dynamic system offers more accurate insights into host health than analyzing microbiome or metabolome data alone.