Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review
Abstract
Recent advances in single cell sequencing and multi-omics techniques have significantly improved our understanding of biological phenomena and our capacity to model them. Despite combined capture of data modalities showing similar progress, notably single cell transcriptomics and proteomics, simultaneous multi-omics level probing still remains challenging. As an alternative to combined capture of biological data, in this review, we explore current and upcoming methods for post-hoc network inference and integration with an emphasis on single cell transcriptomics and proteomics. By examining various approaches, from probabilistic models to graph-based algorithms, we outline the challenges and potential strategies for effectively combining biological data types while simultaneously highlighting the importance of model validation. With this review, we aim to inform readers of the breadth of tools currently available for the purpose-specific generation of heterogeneous multi-layer networks.
Keywords
Cite
@article{arxiv.2503.09568,
title = {Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review},
author = {Marcello Barylli and Joyaditya Saha and Tineke E. Buffart and Jan Koster and Kristiaan J. Lenos and Louis Vermeulen and Vivek M. Sheraton},
journal= {arXiv preprint arXiv:2503.09568},
year = {2025}
}
Comments
10 pages, 3 figures, 1 table