With the advent of high-throughput sequencing and omics approaches, biology has become increasingly dependent on data generated at levels such as epigenomics, genomics, transcriptomics, proteomics, and lipidomics, which are collectively known as “multi-omics” data. A comprehensive understanding of human health and diseases including cancers requires interpretation of molecular intricacy and variations at multiple levels. Multi-omics data analysis alongside clinical information is at the forefront of systematic understanding of the functions of human genes and the mechanisms of cancers and other diseases.

In the past few years, various studies have shown that combining omics data sets generates more understanding of the system under study. For example, through multi-omics design, researchers have utilized three types of integrated analyses to identify differences in encoded and expressed microbial functions that are involved in the BPA-degrading microbial community. Furthermore, researchers have also been able to leverage multi-omics and machine learning tools to detect microbial biomarkers from blood and tissues, serving as a great example of microbiome-informed oncology.

This Special Issue focuses on the tools, models, and methods that perform the analysis of multiple omics data and their applications in discovering biomarkers to better understand complex human diseases including cancers.

Potential topics include but are not limited to the following:

• Bioinformatics analysis for multi-omics data
• Novel biomarker discovery
• Comparative genomics and molecular evolution
• Multi-omics data visualization
• Software, web-tools, and databases development
• Single-cell analysis in cancer genomics
• Novel drug target identification
• Genomics markers knowledge discovery
• Advanced statistical methods for medical data analysis
• Text mining in medical records