Evidence map›Paper›PMID 37566077›Full record

ArticleCells2023

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge.

Handan Can, Sree K Chanumolu, Barbara D Nielsen, Sophie Alvarez, Michael J Naldrett, Gülhan Ünlü, Hasan H Otu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Handan CanDepartment of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Sree K ChanumoluDepartment of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Barbara D NielsenDepartment of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID 83844, USA.
Sophie AlvarezProteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.ORCID 0000-0001-8550-2832
Michael J NaldrettProteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.ORCID 0000-0002-6899-5652
Gülhan ÜnlüDepartment of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID 83844, USA.ORCID 0000-0003-2222-2723
Hasan H OtuDepartment of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains:

Indexed as

Cultured Milk ProductsBacteriaMultiomicsProteomicsShikimic AcidshikimateShikimic AcidBayesian networkskefirLactobacillus kefiranofaciensLentilactobacillus kefirimulti-omics

Identifiers

PMID37566077
PMCPMC10417344

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.