Published Paper Details:

Biological processes are inherently complex, requiring an understanding not only of individual components but also of their intricate interconnections. Graphs, or networks, provide a natural framework for representing such relationships, enabling the depiction of both biological entities and their interactions. Recent advancements in high-throughput experimental technologies have led to an exponential increase in the generation of biological network data, which is now readily accessible to the scientific community through online databases facilitated by internet web services. Consequently, there is a growing demand for novel techniques to query, analyze, and process this wealth of data, with the aim of extracting insights into molecular biology, physiology, electronic health records, biological networks, and biomedicine as a whole. Machine learning, owing to its ability to effectively handle large datasets and generate accurate predictions using sophisticated statistical models, has emerged as a powerful tool for meeting these demands, promising rapid growth and widespread utilization in biomedical research.