Methods, Tools and Algorithms in Network Bioinformatics

Network bioinformatics sits at the intersection of graph theory, machine learning, and molecular biology, providing a principled framework to model complex biological systems as networks of interacting components. From gene regulatory and protein–protein interactions to metabolic, signaling, and cell–cell communication, network representations enable integrative analyses across multi-omics, spatial, and single-cell data. As datasets grow in depth and breadth, core challenges persist: inferring reliable, context-specific networks from noisy measurements; integrating heterogeneous data modalities and prior knowledge; capturing dynamics and causality under perturbations; and ensuring scalability to graphs with millions of nodes and edges without sacrificing interpretability.

Recent advances in probabilistic modeling, causal discovery, and graph machine learning (including graph neural networks and self-supervised approaches) have opened new paths to predict missing links, identify key regulators, and extract mechanistic hypotheses. Equally important are standards and tools: interoperable formats, benchmarking resources, and reproducible workflows that make methods comparable and usable by the broader community. Higher-order network models (multiplex, hypergraphs, and simplicial complexes) promise to encode multi-way and condition-specific interactions more faithfully than pairwise graphs, while uncertainty quantification and robustness analyses are becoming essential for credible translational applications. Clinically, network-based strategies support biomarker discovery, patient stratification, target prioritization, and drug repurposing through diffusion, controllability, and proximity analyses. This Research Topic seeks to consolidate methodological innovation with practical tooling and rigorous evaluation, promoting approaches that are scalable, transparent, and biologically meaningful.
We especially welcome contributions that bridge in silico models with experimental validation, advancing networks from descriptive maps to causal, actionable frameworks in health and disease.

To advance the methodological, algorithmic, and tooling foundations of network bioinformatics for constructing, analyzing, and interpreting biological networks, with emphasis on scalability, interpretability, dynamics, and reproducibility. We aim to unify best practices, benchmarks, and open-source implementations that translate network insights into robust biological and clinical impact.

We invite Mini Reviews, Systematic Reviews, and Full Reviews across these subthemes:

- Network inference and integration: methods for GRN and signaling inference; fusion of multi-omics, single-cell, and spatial data; incorporation of pathways and ontologies; cross-condition and cross-species alignment; transfer and federated learning.

- Dynamics and causality: time-series and perturbation-based modeling (CRISPR, drugs); causal discovery under interventions; hybrid mechanistic–ML approaches; uncertainty quantification and calibration.

- Graph machine learning: interpretable GNNs, attention mechanisms, contrastive/self-supervised learning; embeddings for function prediction, module discovery, and link prediction; handling heterogeneity, multiplex, and hypergraph structures.

- Scalability and efficiency: algorithms for large, sparse, and streaming networks; distributed/parallel computation; energy-efficient and hardware-aware implementations.

- Higher-order and spatial networks: hypergraphs and simplicial complexes; spatially resolved and cell–cell interaction networks; community structure, motifs, controllability, and network rewiring.

- Tools, standards, and reproducibility: interoperable data formats, FAIR principles, benchmarking suites, leaderboards, and workflow languages; containerized, provenance-aware pipelines; visualization and interactive exploration platforms.

- Translation and applications: network-based biomarkers, patient-specific networks, drug repurposing and combination therapy design; applications in oncology, immunology, neurology, infectious disease, and microbiome–host systems.

Reviews should emphasize methodological taxonomy, comparative evaluation, best practices, open datasets/software, and open challenges to guide future work.