Evidence map›Paper›PMID 41332478›Full record

ReviewBiotechnology reports (Amsterdam, Netherlands)2025

Integrating multi-omics data: Methods and applications in human complex diseases.

Pasquale Sibilio, Enrico De Smaele, Paola Paci, Federica Conte

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biotechnology reports (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

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

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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.

Pasquale SibilioTranslational Oncology Research Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy.
Enrico De SmaeleDepartment of Experimental Medicine, Sapienza University of Rome, Rome, Italy.
Paola PaciDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Federica ConteInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past few decades, technological advancements and declining costs of high-throughput data generation have revolutionized biomedical research, enabling the collection of large-scale datasets across multiple omics layers-including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. The analysis and integration of these datasets provides global insights into biological processes and holds great promise in elucidating the myriad molecular interactions associated with human diseases, particularly multifactorial ones such as cancer, cardiovascular, and neurodegenerative disorders. However, integrating multi-omics data presents significant challenges due to high dimensionality and heterogeneity. This review explores computational methods for integrating multi-omics data, with a particular focus on network-based approaches that offer a holistic view of relationships among biological components in health and disease. Furthermore, this review showcases a selection of recent, successful applications of multi-omics data integration, moving beyond theoretical methods to demonstrate their transformative potential in biomarker discovery, patient stratification, and guiding therapeutic interventions in specific human diseases.

Indexed as

Human complex diseasesIntegrative approachesMulti-omics data

Identifiers

PMID41332478
PMCPMC12666689

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.