Advances in Bioinformatics for the Functional Interpretation of Multi-omic Experimental Data

The field of bioinformatics has been revolutionized by the rapid emergence of multi-omic technologies, including genomics, transcriptomics, proteomics, metabolomics, and the growing landscape of epigenomic, single-cell, and spatial omic platforms. While these technologies generate vast volumes of high-dimensional biological data, their true potential lies in identifying meaningful biological functions underlying complex systems. Contemporary studies have demonstrated that integrating multi-omic datasets reveals layers of regulation, novel biomarkers, disease mechanisms, and context-specific interactions not evident from single-omic analyses. However, challenges remain in translating these integrated data into interpretable, actionable biological insights. Current limitations include computational scalability, reliable annotation, harmonization across diverse data types, and accurately linking molecular variation to functional consequences. As the field advances, there is urgent need for innovative computational frameworks and methodological best practices that bridge the gap between data generation and functional understanding.
This Research Topic aims to spotlight innovations that enhance the functional interpretation of multi-omic experimental data through advanced bioinformatics methodologies. The objective is to assemble research that addresses unresolved questions such as how to reliably map multi-layered molecular information to functional outcomes, how to disentangle causal relationships in complex networks, and how to ensure the interpretability and translation of omic findings to clinical or biotechnological contexts. We welcome contributions that present novel algorithms, statistical models, simulation approaches, machine learning and AI methodologies, tools for data integration and harmonization, as well as case studies where bioinformatics facilitates functional discovery from multi-omic experiments.
This Research Topic covers manuscripts investigating the functional interpretation of multi-omic datasets by leveraging integrative bioinformatics approaches, while focusing on analytical transparency and biological relevance. There are no methodological restrictions, but submissions should emphasize innovation, generalizability, and robust validation.
To gather further insights in this area, we welcome articles addressing, but not limited to, the following themes:
• Development and benchmarking of computational tools for multi-omic data integration
• Machine learning, deep learning, and AI models for predicting biological function from omic data
• Novel approaches to pathway, network, and causal analysis in multi-omic contexts
• Strategies for ensuring reproducibility and interpretability in functional bioinformatics
• Translational applications linking multi-omic data to clinical or environmental outcomes
• Visualization and reporting standards for multi-omic functional studies
• Single-cell and spatial omics functional interpretation frameworks
• Databases, resources, and open-source platforms facilitating multi-omic functional analysis