Methods for multi-omic data integration in cancer research
Por:
Hernández-Lemus E., Ochoa S.
Publicada:
1 ene 2024
Resumen:
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. Copyright © 2024 Hernández-Lemus and Ochoa.
Filiaciones:
Hernández-Lemus E.:
Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
Center for Complexity Sciences, Universidad Nacional Autónoma de México, México City, Mexico
Ochoa S.:
Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
gold, All Open Access; Gold Open Access
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