Insights in Network Bioinformatics

Biological systems function through complex interactions among genes, proteins, cells and biological processes, making network-based approaches essential for understanding how molecular components generate phenotype. Network bioinformatics brings together graph theory, systems biology, machine learning and molecular biology to infer, analyze and model these interactions. Recent advances have improved the reconstruction of gene regulatory, signaling and protein-protein interaction networks, while network medicine has enabled the identification of disease modules, therapeutic targets and biomarkers. At the same time, graph representation learning and network embeddings are expanding the ability to extract biological meaning from high-dimensional and heterogeneous datasets. However, challenges remain in ensuring inference accuracy, biological interpretability and robustness across experimental conditions and data sources. The increasing availability of multi-omics, single-cell and spatial datasets further highlights the need for scalable methods capable of capturing the dynamic, multilayered and context-dependent nature of biological networks. Continued investigation is therefore needed to establish reliable, reproducible and biologically informed network bioinformatics frameworks.This Research Topic aims to provide an overview of recent advances in network bioinformatics and to examine how network inference, analysis and modeling can clarify emergent biological behavior. It will explore how network-based approaches can connect molecular interactions to cellular processes and organismal phenotypes, prioritize disease-associated mechanisms and identify potential therapeutic targets. Contributions may address methodological developments, applications to diverse biological systems, and the integration of machine learning with mechanistic or knowledge-based models. The Research Topic will also consider how the field can address persistent challenges related to data quality, inference uncertainty, computational scalability, validation, interpretability and reproducibility.To gather further insights into computational and experimental approaches for modeling complex biological systems as networks, we welcome articles addressing, but not limited to, the following themes:• Inference of gene regulatory and cell-signaling networks• Protein-protein interaction networks and functional complexes• Network medicine, disease modules and therapeutic target prioritization• Graph machine learning, graph neural networks and network embeddings• Multilayer, dynamic, temporal and spatial biological networks• Network-based integration and interpretation of multi-omics data• Single-cell and spatially resolved biological network analysis• Network robustness, controllability, causality and perturbation analysis• Biological knowledge graphs and semantic network representations• Open, reproducible tools, datasets and benchmarks for network analysisThis collection welcomes contributions reflecting on current developments and plotting pathways for upcoming research, from Editorial Board Members of the section, from authors they refer, and, on invitation, from a small number of external researchers whose work is shaping the direction of the field. Authors are encouraged to engage critically with their fields, identify the current challenges and propose novel solutions that advance bioinformatics tools and their application to real biological problems. Topic editors: Jihwan Ha, Jianhua Ruan, Hongyan Wu Source: https://www.frontiersin.org/research-topics/84356/insights-in-network-bioinformatics