Graph Embedding Methods for Multiple-Omics Data Analysis

There has been an increasing growth of complex multiple-omics data sets due to the advent of advanced high throughput biotechnologies such as single-cell sequencing and Next-Generation sequencing. In contrast to the traditional single omics approach, it aims to identify causative connections rather than consequential changes. This yields a global view by integrating the information derived independently from single genomic, transcriptomic, proteomic, and metabolomic levels. For example, this approach can further assist in designing better diagnostic tools and therapies for the treatment of diseases. Thus, the multi-omics data analysis is important to offer more evidence for exploring biological mechanisms.

Graph embedding methods have shown powerful capability in analyzing multiple-omics data, alongside genetic, phenotypic, and environmental factors-based approaches. However, there remain challenges and gaps between computer theories and real-world application requirements, the integration of multi-omics data from different technical platforms for instance. Therefore, this gives rise to the increasing demand for applications of the graph embedding methods to multiple-omics data analyses.

This Research Topic intends to provide an international forum for researchers to showcase their up-to-date computational methods for multiple-omics data analysis. We invite submissions of high-quality papers on original research, which have not been published previously.

Topics of interest include, but not limited to, graph embedding methods for the analysis of:
• Genomics data
• Proteomics data
• Metabolomics data
• Transcriptomics data
• Lipidomics data
• Immunomics data
• Glycomics data
• Multi-omics data fusion