Figure 3.

A step-by-step illustration of the application of AI methodologies employed, and how they integrate with immune-associated signatures and impact patient outcomes. AI neural networks can be used to predict immune responses by simulating immune reactions to tumors. This leads to better clinical decisions, such as the use of antibodies and immune checkpoints (like PD-1/PD-L1), or to preventing the recurrence of the disease. Characterizing the patient’s immune signature through AI allows for the development of new antibody therapies. Machine learning (ML); differentially expressed genes (DEGs).