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Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines, , , , , , , , , and 730 other author(s). Cell Systems, 6 (3): 271--281.e7 (March 2018)Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer., , , , , , , , , and 11 other author(s). Cell Rep, 8 (3): 798--806 (August 2014)Deep Learning-Based Bias Transfer for Overcoming Laboratory Differences of Microscopic Images., , , , , , , , , and 2 other author(s). MIUA, volume 12722 of Lecture Notes in Computer Science, page 322-336. Springer, (2021)Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer., , , , , , , , , and 50 other author(s). Cancer Cell, 23 (2): 159--170 (February 2013)Black-Box Hyperparameter Optimization for Nuclei Segmentation in Prostate Tissue Images., , , , , , , , , and . Bildverarbeitung für die Medizin, page 345-350. Springer Vieweg, (2019)TMPRSS2-ERG fusions are strongly linked to young patient age in low-grade prostate cancer., , , , , , , , , and 10 other author(s). Eur Urol, 66 (6): 978--981 (December 2014)Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines, , , , , , , , , and 730 other author(s). Cell Systems, 6 (3): 271--281.e7 (March 2018)Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients, , , , , , , , , and 725 other author(s). Cancer Cell, 34 (2): 211--224.e6 (August 2018)Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network., , , , , , , and . ML4H@NeurIPS, volume 158 of Proceedings of Machine Learning Research, page 38-53. PMLR, (2021)