Predicting Breast Cancer Survival Rate Based on Genetic Data: A Machine Learning Approach
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62502-2_46
Reference4 articles.
1. Mahvi, D.A., Liu, R., Grinstaff, M.W., Colson, Y.L., Raut, C.P.: Local cancer recurrence: the realities, challenges, and opportunities for new therapies. CA Cancer J. Clin. 68(6), 488–505 (2018). https://doi.org/10.3322/caac.21498
2. Clark, B.Z., Onisko, A., Assylbekova, B., Li, X., Bhargava, R., Dabbs, D.J.: Breast cancer global tumor biomarkers: a quality assurance study of intratumoral heterogeneity. Mod. Pathol. 32(3), 354–366 (2019). https://doi.org/10.1038/s41379-018-0153-0
3. Peintinger, F., Reitsamer, R., Smidt, M.L., Kühn, T., Liedtke, C.: Lymph nodes in breast cancer - What can we learn from translational research. Breast Care 13(5), 342–347 (2018). https://doi.org/10.1159/000492435
4. Kitamura, M., et al.: Progression potential of ductal carcinoma in situ assessed by genomic copy number profiling. Pathobiology 86(2–3), 92–101 (2019). https://doi.org/10.1159/000492833
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