Editorial Advances in Computational Immunology

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational immunology and immunological bioinfor-matics are firm and quickly growing research fields. Whereas the former aims to develop mathematical and/or computa-tional methods to study the dynamics of cellular and molec-ular entities during the immune response [1–4], the latter focuses on proposing methods to investigate big genomic and proteomic immunological-related datasets and predict new knowledge mainly by statistical inference and machine learning algorithms. The glut of data produced by high-throughput instru-mentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, requires computational tools for acquisition, storage, and analysis of immunological data