The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.

Our goal is to publish sound science. This means we look for research that adheres to ethical standards, uses robust methodologies that are transparent and reproducible, and uses appropriate statistical analyses to ensure clear, credible, and trustworthy results. We care more about the quality and reliability of the contribution an article makes than we do the size of that contribution. Cancer Informatics evaluates manuscripts based on how the research was conducted and reported.