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Introducing the companion paper
Scientific research is increasingly relying on large quantities of data, and these data are being analyzed with increasingly sophisticated computational methods (including deep learning and other artificial intelligence systems). The methods used in data-intensive science can be innovative and ground-breaking from a data science point of view, and therefore deserve to be shared across domain boundaries.
Patterns will partner with other Cell Press journals to provide a place where the in-depth technical details of the data science used to investigate key scientific questions can be published and evaluated by experts in those techniques. This will allow the discipline-specific journal to focus on the overview of the experimental design and the implications of the results, secure in the knowledge that the data science methods used have been rigorously peer-reviewed and validated. At the same time the authors will benefit from their important data science work being seen and understood by a wider range of researchers, increasing the impact of their research.
Here are a few examples of papers published in Cell Press journals that we believe may also be suitable for a Patterns companion paper.
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 | Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases
Dmitriy Gorenshteyn, Elena Zaslavsky, Miguel Fribourg, Christopher Y. Park, Aaron K. Wong, Alicja Tadych, Boris M. Hartmann, Randy A. Albrecht, Adolfo García-Sastre, Steven H. Kleinstein, Olga G. Troyanskaya, Stuart C. Sealfon
 Immunity |
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 | A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications
George W. Wright, Da Wei Huang, James D. Phelan, Zana A. Coulibaly, Sandrine Roulland, Ryan M. Young, James Q. Wang, Roland Schmitz, Ryan D. Morin, Jeffrey Tang, Aixiang Jiang, Aleksander Bagaev, Olga Plotnikova, Nikita Kotlov, Calvin A. Johnson, Wyndham H. Wilson, David W. Scott, Louis M. Staudt
Cancer Cell |
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 | Patterns promotes all types of research outputs and facilitates sharing and collaboration to solve key scientific problems and aid in the development of data science solutions for practice, policies, and management.
The groundbreaking data science research we publish is both theoretical and practical. We’re committed to innovations that make research actionable for humans and machines alike. Patterns is domain agnostic and offers breadth and depth across the spectrum of research disciplines, including the humanities and computational, physical, life, and social sciences. |
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 | Related Journals from Cell Press
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