Evidence map›Paper›PMID 26396492›Full record

ReviewBiomarker insights2015

Genomic, Proteomic, and Metabolomic Data Integration Strategies.

Kwanjeera Wanichthanarak, Johannes F Fahrmann, Dmitry Grapov

Abstract readReview
In one paragraph

Review in Biomarker insights, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 1 pooled it
–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

73 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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13 more citing papers are in PubMed but not listed here.

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

3 authors.

Kwanjeera WanichthanarakUniversity of California, Genome Center, Davis, CA, USA.
Johannes F FahrmannUniversity of California, Genome Center, Davis, CA, USA.
Dmitry GrapovCDS Creative Data Solutions, Ballwin, MO, USA.

Funding

West Coast Central Comprehensive Metabolomics Resource Core (WC3MRC)U24DK097154 · NIDDK · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FIEHN, OLIVER · 2012 to 2017
$9.9M
NIDDK NIH HHS U24 DK097154
6 · The paper itself

Abstract

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods.

Indexed as

bioinformaticsdata analysisdata integrationgenomicsmetabolomicsnetworksomicsproteomics

Identifiers

PMID26396492
PMCPMC4562606

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.