July 2024 PMID 39020083

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer.

Thejus T Jayakrishnan, Naseer Sangwan, Shimoli V Barot, Nicole Farha, Arshiya Mariam, Shao Xiang, Federico Aucejo, Madison Conces, Kanika G Nair, Smitha S Krishnamurthi, Stephanie L Schmit, David Liska, Daniel M Rotroff, Alok A Khorana, Suneel D Kamath

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Mentions

Peak: 8 in Jul 2024

across 5 sources since Jul 2024

Extended reach

34,397

potential audience

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Jul 2024 – Aug 2024

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News62 articles
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Reddit2 posts
YouTube1 videos

AI synopsis

What people are saying

The group generally supports the study’s innovative multi-omics approach, as evidenced by comments on its potential to uncover biological patterns, with examples like metabolites glycerol and pseudouridine’s correlations with specific bacteria. The tone combines appreciation for the scientific methods with cautious optimism about future therapeutic implications, showcasing a balance of engagement and realistic expectations.

AgreementModerate agreement

Most discussions recognize the value of applying multi-omics machine learning methods to understand early-onset colorectal cancer, indicating general support for the research approach.

InterestHigh interest

The discussions show high interest, especially with mentions of specific metabolites and microbiome interactions, highlighting curiosity about the biological insights gained.

EngagementModerate engagement

Participants engage by referencing specific findings like correlations between metabolites and microbial communities, demonstrating analytical involvement.

ImpactModerate impact

While the research is viewed as promising, discussions suggest cautious optimism about its immediate clinical application, reflecting moderate perceived impact.

Abstract in brief

The incidence of early-onset colorectal cancer (eoCRC) is increasing, yet its causes remain unclear, leading researchers to explore machine learning techniques to uncover differences between eoCRC and average-onset CRC (aoCRC).
Researchers analyzed data from 64 individuals with colorectal cancer, using advanced technologies like plasma metabolomics and microbiome sequencing, and found that a machine-learning model based on metabolomics outperformed the microbiome-based model in identifying unique features associated with eoCRC.
Key findings included specific metabolites that correlated with different microbial communities, suggesting that multi-omics approaches not only reveal distinct biological patterns for eoCRC but also hint at potential therapeutic targets for treatment.

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Uncovering Host-Microbiome Interactions in Early-Onset Colorectal Cancer Using Machine Learning

This video explores how multi-omics and machine learning are used to study host-microbiome interactions in early-onset colorectal cancer, revealing distinct biological patterns and potential therapeutic targets.

DrOmics Labs Pvt Ltd · Aug 24, 2024 0 views

サイエンスあれこれ

@sarekore · X

実際にがんリスクにどのように影響するのかについては明らかにされてはいませんが、大腸がんの早期発見用診断マーカーとして有望です。https://t.co/D2AlND2Okj

Aug 31, 2024 · 922 followers View post →

Wafik S. El-Deiry, MD, PhD, FACP

@weldeiry · X

Aug 24, 2024 · 18,414 followers View post →

drjasonmccloskey

@drjmccloskey · X

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer | npj Precision Oncology https://t.co/UhkI6YyVvG

Aug 19, 2024 · 78 followers View post →

Altamiro Bottino

@BottinoAltamiro · X

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/jgGXK9ie4h https://t.co/bTdZNYqnea

Aug 17, 2024 · 93 followers View post →

Yasuhisa Nagasaki＠NERON, RIKEN

@Neron_Ngsk32 · X

RT @yasuhisanagasa1: Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/Gqxm4…

Jul 25, 2024 · 1 share · 4,810 followers View post →

yasuhisa nagasaki

@yasuhisanagasa1 · X

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/Gqxm4fJyF0 https://t.co/JIcWJj9Ep7

Jul 25, 2024 · 1 share · 59 followers View post →

A.n.n.

@fireflyann · X

RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf

Jul 18, 2024 · 3 shares · 1,582 followers View post →

Roberta Horgan

@HorganRoberta · X

RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf

Jul 18, 2024 · 3 shares · 2,609 followers View post →

lynch syndrome irl @lsireland.bsky.social

@Lynchsyndromirl · X

RT @josepereag: Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf

Jul 18, 2024 · 3 shares · 2,397 followers View post →

JOSÉ PEREA GARCÍA

@josepereag · X

Multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf

Jul 18, 2024 · 5 likes · 3 shares · 1,242 followers View post →

JOSÉ PEREA GARCÍA

@josepereag · X

multi-omics analysis can be utilized to study host-microbiome correlations in #EOCRC https://t.co/gRbfRcwgFf

Jul 18, 2024 · 1,242 followers View post →

LUKMAN AFOLABI

@saintlukman · X

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer https://t.co/64Hf49qG3w https://t.co/nFlk3sr1Bt

Jul 17, 2024 · 921 followers View post →

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer.

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Uncovering Host-Microbiome Interactions in Early-Onset Colorectal Cancer Using Machine Learning