Emergence of graph theory-based biomedical signal analysis

The graph-based analysis of complex networks has emerged as a powerful mathematical tool to explore natural, social, and biological systems. The potential of graph features in reflecting the interconnected elements in a complex system makes it suitable for analyzing various biological phenomena and thereby enabling it as a diagnostic tool. Today, graph theory finds extensive application in biomedical signal and image processing. The present review deciphers the potential biomedical applications of graph theory, starting from the fundamentals. Special emphasis has been given to the application of graph theory to cancer, brain, cardiovascular, and protein–protein networks. The systematic approach of evaluating the literature through the PRISMA 2020 standards is followed to map the research done in this domain and identify the gaps in the field. The clinical relevance of graph-based disease detection with machine learning makes the identifying, predicting, and prognostic treatments of various illnesses easier. Novel developments in the examination and characterization of the network topologies of several biological networks indicate that complex networks may play a role in the early detection and treatment of a range of diseases. Amid challenges like large dataset handling capability, precise gene detection, and targeted drug delivery, future directions in this field would involve exploring graph-based deep learning and transfer learning techniques to analyze complex biological information, providing predictive treatment for various maladies.