antibodies against viral pathogens. These examples illustrate the power of immunoinformatics applications to help solve complex problems in public health. Immunoinformatics could accelerate the discovery process dramatically and potentially shorten the time required for vaccine development. Immunoinformatics tools have been used to design the vaccine against SARS-CoV-2, Dengue virus and Leishmania. Immune system function Using this technology it is possible to know the model behind immune system. It has been used to model T-cell-mediated suppression, peripheral lymphocyte migration, T-cell memory, tolerance, thymic function, and antibody networks. Models are helpful to predicts dynamics of pathogen toxicity and T-cell memory in response to different stimuli. There are also several models which are helpful in understanding the nature of specificity in immune network and immunogenicity. For example, it was useful to examine the functional relationship between TAP peptide transport and HLA class I antigen presentation. TAP is a transmembrane protein responsible for the transport of antigenic peptides into the endoplasmic reticulum, where MHC class I molecules can bind them and presented to T cells. As TAP does not bind all peptides equally, TAP-binding affinity could influence the ability of a particular peptide to gain access to the MHC class I pathway. Artificial neural network (ANN), a computer model was used to study peptide binding to human TAP and its relationship with MHC class I binding. The affinity of HLA-binding peptides for TAP was found to differ according to the HLA supertype concerned using this method. This research could have important implications for the design of peptide based immuno-therapeutic drugs and vaccines. It shows the power of the modeling approach to understand complex immune interactions. There exist also methods which integrate peptide prediction tools with computer simulations that can provide detailed information on the immune response dynamics specific to the given pathogen's peptides . Cancer Informatics Cancer is the result of somatic mutations which provide cancer cells with a selective growth advantage. Recently it has been very important to determine the novel mutations. Genomics and proteomics techniques are used worldwide to identify mutations related to each specific cancer and their treatments. Computational tools are used to predict growth and surface antigens on cancerous cells. There are publications explaining a targeted approach for assessing mutations and cancer risk. Algorithm CanPredict was used to indicate how closely a specific gene resembles known cancer-causing genes. Cancer immunology has been given so much importance that the data related