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Multi-omics Data Integration, Interpretation, and Its Application

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Subramanian, Indhupriya, Verma, Srikant, Kumar, Shiva, Jere, Abhay, Anamika, Krishanpal·2020·Bioinformatics and Biology Insights
DOI 10.1177/1177932219899051PMC PMC7003173
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Abstract

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

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  • These methods can be divided into categories, such as Bayesian integration, network and pathway integration, fusion-based integration, similarity-based integration, and other multivariate approaches.

    “These methods can be divided into categories, such as Bayesian integration (e.g., Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) [97], iClusterPlus [98], Patient-specific data fusion (PSDF) [99]), network and pathway integration (e.g., Similarity network fusion (SNF) [100], PaintOmics [101], MultiGSEA [102], multi-omics gene-set analysis (MOGSA) [103], Cytoscape”

    From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [111]

  • A large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.

    “Along with the introduction of high-throughput technologies and the availability of multi-omics data derived from a number of samples, a large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.”

    From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [111]

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Cited by

Sorted by citations

Authors
Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K.
Affiliations
  • Persistent Systems (India) (IN)
  • Ministry of Human Resource Development (IN)
Corresponding author
Anamika, Krishanpal
ORCID
Venue
Bioinformatics and Biology Insights
Publisher
SAGE Publications
Published
2020-01
Volume
14
Pages
117793221989905
ISSN
1177-9322 (ISSN-L)
DOI
10.1177/1177932219899051
PMC
PMC7003173
OpenAlex
W3003192283
Cited by
1,609
References
10
Citations by year
2026: 318 · 2025: 335 · 2024: 327 · 2023: 223 · 2022: 229 · 2021: 133 · 2020: 43 · 2018: 1
FWCI
15.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics and Biology Insights (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
multi-omics integration, disease subtyping, biomarker prediction

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