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Review
. 2024 Mar 27;25(3):bbae213.
doi: 10.1093/bib/bbae213.

Towards multi-omics synthetic data integration

Kumar Selvarajoo  1   2   3 , Sebastian Maurer-Stroh  1   2
Affiliations
Review

Towards multi-omics synthetic data integration

Kumar Selvarajoo et al. Brief Bioinform. .

Abstract

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Keywords: data-driven; machine learning; multi-omics; process-driven; synthetic data.

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Figures

Figure 1
Figure 1
Synthetic data generation. (A) Process-driven. Left: for bulk cells an initial signaling model is developed using known mechanistic biochemical reactions and the corresponding experimental data for TRAIL signaling [6]. Right: the bulk validated TRAIL model is used to generate 1000 single cells data synthetically [8]. (B) Data-driven. Left: statistical models are used to general transcriptome-wide expression data scatter plots for bulk and single cells [10]. Right: a VAE-based model used to generate UMAP plots of 68 K single cells data, top (real) and bottom (synthetic using ACTIVA [11]).
See this image and copyright information in PMC

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