Multi-omics Data Integration, Interpretation, and Its Application
By Indhupriya Subramanian, S. Verma, Shiva Kumar, A. Jere, K. Anamika. Bioinformatics and Biology Insights, 2020
1,199 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/bacec2bca97cfe6265d7cfc75fafcb34b47aa36d
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Citations in the corpus
- Changes in Protein Structural Motifs upon Post-Translational Modification in Kidney Cancer (2021, background): “Multidimensional omics data on the level of gene expression, transcript content, protein and metabolite composition and content provide a vast array of knowledge that requires further investigation to understand the molecular events accompanying the pathological process [1].”