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  • br Author contributions br H

    2019-09-16


    Author contributions
    H.S. designed the study, analyzed the data, and developed mPS. K.I.N. coordinated the study and wrote the manuscript. Both authors read and approved the final version of the manuscript.
    Declaration of Competing Interest
    The authors declare no potential conflicts of interest.
    Acknowledgments
    We thank S. Miyano for comments on computational and statistical methods; K. Mimori for advice on the clinical aspects of breast cancer; S. Fujinuma, Y. Yamauchi, and other laboratory members for discussion; and A. Ohta for help with preparation of the manuscript.
    References
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    [21] Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015; 13:8–17.
    [22] Prabhakaran S, Rizk VT, Ma Z, et al. Evaluation of invasive breast cancer samples using a 12-chemokine gene expression score: correlation with clinical outcomes. Breast Cancer Res 2017;19:71.
    [23] Brueffer C, Vallon-Christersson J, Grabau D, et al. Clinical value of RNA sequencing– based classifiers for prediction of the five conventional breast cancer biomarkers: a report from the population-based multicenter Sweden cancerome analysis net-work—breast initiative. JCO Precis Oncol 2018. https://doi.org/10.1200/PO.17.00135. [24] Jezequel P, Campone M, Gouraud W, et al. bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer. Breast Cancer Res Treat 2012;131:765–75.
    Please cite this article as: H. Shimizu and K.I. Nakayama, A 23 gene–based molecular prognostic score precisely predicts overall survival of breast cancer pati..., EBioMedicine, https://doi.org/10.1016/j.ebiom.2019.07.046