Evidence map›Paper›PMID 36252928›Full record

ReviewBriefings in bioinformatics2022

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration.

Zeynab Maghsoudi, Ha Nguyen, Alireza Tavakkoli, Tin Nguyen

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

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

  1. Pooled it
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  15. Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025
    Review
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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

4 authors.

Zeynab MaghsoudiDepartment of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA.
Ha NguyenDepartment of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA.
Alireza TavakkoliDepartment of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA.
Tin NguyenDepartment of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA.

Funding

National Science Foundation 2001385
6 · The paper itself

Abstract

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Indexed as

GenomicsProteomicsBiomarkersTranscriptomeBiomarkersintegrative pathway analysismulti-cohort analysismulti-omics integrationpathway graph transformation

Identifiers

PMID36252928
PMCPMC9677478

What OpenQuestion holds

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LicenceCC BY-NC
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.