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REVIEW article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1425456
This article is part of the Research Topic Computational Methods and Novel Applications for Pathway Analysis Using High-throughput Biological Data View all articles

Methods for multi-omic data integration in cancer research

Provisionally accepted
Enrique Hernandez-Lemus Enrique Hernandez-Lemus *Soledad Ochoa Soledad Ochoa
  • Computational Genomics, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico

The final, formatted version of the article will be published soon.

    Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy.However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature.Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

    Keywords: multi-omics, data integration, statistical and probabilistic modelling, Regulatory models, LASSO, cancer biology

    Received: 29 Apr 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Hernandez-Lemus and Ochoa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Enrique Hernandez-Lemus, Computational Genomics, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.