Selvarajoo, K., & Maurer-Stroh, S. (2024). Towards multi-omics synthetic data integration. Briefings in Bioinformatics, 25(3). https://doi.org/10.1093/bib/bbae213
Abstract:
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.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: ASTAR - Bioinformatics Institute (BII).
Description:
This is a pre-copyedited, author-produced version of an article accepted for publication in Briefings in Bioinformatics following peer review. The version of record Selvarajoo, K., & Maurer-Stroh, S. (2024). Towards multi-omics synthetic data integration. Briefings in Bioinformatics, 25(3). https://doi.org/10.1093/bib/bbae213 is available online at: https://doi.org/10.1093/bib/bbae213