A Systematic Review of Topology-Based Models for Protein–Protein Interaction Networks: Methods, Architectures, and Future Research Directions
Bibliographic record
Abstract
Protein–protein interaction (PPI) networks play a crucial role in understanding cellular processes, disease mechanisms, and drug discovery. Topology-based models have emerged as powerful tools for analysing the structural and functional organization of these networks by leveraging graph theory, network science, and topological data analysis. This systematic review examines advances from 2018 to 2023 in topology-based modelling of PPI networks, focusing on methodologies, computational architectures, and emerging research directions. The review explores classical graph-theoretic approaches, including centrality measures, clustering, and modularity detection, alongside advanced techniques such as persistent homology, network embedding, and graph neural networks (GNNs). Special attention is given to algorithms for protein complex detection, topological scoring, and multi-scale network analysis. The findings indicate that while traditional topological models provide strong interpretability and biological relevance, modern hybrid approaches integrating machine learning and topological features significantly enhance prediction accuracy and scalability. However, challenges remain in handling noisy datasets, dynamic interactions, and computational complexity. Future research directions include topology-driven deep learning frameworks, multi-layer biological networks, and interpretable AI models for PPI analysis. This review provides a comprehensive foundation for developing next-generation topology-aware computational frameworks in systems biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".