For the second year in a row, we are very happy to offer our readership an ebook of 10 articles that have achieved widespread acceptance within our core audience and beyond. This time it concerns articles published in 2023, a landmark year for this journal, as it was officially awarded its first impact factor. These papers are among the large number that attained significant interest last year, but we selected just 10, which we consider to be the “best”. These articles have already made an impact in the form of original research or comprehensive reviews. As the Field Chief Editor, I would like to stand alongside our journal staff to honor all authors who contributed very high-level papers to the journal last year and are contributing to our success. We also thank the editors and reviewers of these papers, and of all papers this past year, for their invaluable contribution.

Immunoinformatics of Cancers: Practical Machine Learning Approaches Using R takes a bioinformatics approach to understanding and researching the immunological aspects of malignancies. It details biological and computational principles and the current applications of bioinformatic approaches in the study of human malignancies. Three sections cover the role of immunology in cancers and bioinformatics, including databases and tools, R programming and useful packages, and present the foundations of machine learning. The book then gives practical examples to illuminate the application of immunoinformatics to cancer, along with practical details on how computational and biological approaches can best be integrated.This book provides readers with practical computational knowledge and techniques, including programming, and machine learning, enabling them to understand and pursue the immunological aspects of malignancies. Presents the knowledge researchers need to apply computational techniques to immunodeficiencies Provides the most practical material for bioinformatics approaches to the immunology of cancers Gives straightforward and efficient explanations of programming and machine learning approaches in R Includes details of the most useful databases, tools, programming packages and algorithms for immunoinformatics Illuminates clear explanations with practical examples of immunoinformatic approaches to cancer