ABSTRACT

Introduction: A robust defence system is vital for survival of any organism. A constant battle exists between a potential pathogen and the host. The outcome depends on how well-built the host immune system is. Mammalian immune system is structured as a network right from the molecular to cellular level and finally at the level of the whole organism. These networks range from gene/protein interactions at molecular level (contributing to intracellular signalling pathways) to more complex interactions at in vivo level.
Objective: The current study is aimed towards understanding the existing AI-based tools which can be used for studying immune signalling pathway behaviours, understanding immune perturbations in disease conditions, handling big immunology datasets and specific immunomodulatory therapies.Any change in immune response can be traced back to the disturbances in the concerted effort of any one/multiple such networks. Methodology: The discussion then transitions to how AI technologies—such as machine learning and network modeling—enable unprecedented analysis of large-scale immunological data, revealing intricate signaling dynamics and novel biomarkers. The abstract underscores key breakthroughs where AI-driven approaches have redefined the mechanistic insights into immune responses and disease progression.
Findings: These tools provide insight into response pattern visualization as well as help predict interactions which may not have previous documentation through bench work. Thus these tools are a great resource as they help understand how the signalling network components work and how they might interact under a given situation, thereby saving a lot of resources/time/effort. Pathway modelling can contribute significantly in understanding molecular mechanisms of various diseases. They can also aide in developing focused therapies. Implications: Finally, it addresses future prospects and implications for precision medicine, emphasizing AI’s potential to revolutionize diagnostic and therapeutic strategies by integrating systems-level immune network analysis