Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019).

Article  PubMed  Google Scholar 

Bentellis, I., Guerin, S., Khene, Z. E., Khavari, R. & Peyronnet, B. Artificial intelligence in functional urology: how it may shape the future. Curr. Opin. Urol. 31, 385–390 (2021).

Article  PubMed  Google Scholar 

Brodie, A. et al. Artificial intelligence in urological oncology: an update and future applications. Urol. Oncol. 39, 379–399 (2021).

Article  PubMed  Google Scholar 

Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Lo Vercio, L. et al. Supervised machine learning tools: a tutorial for clinicians. J. Neural Eng. 17, 062001 (2020).

Article  Google Scholar 

Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374, 20150202 (2016).

PubMed  PubMed Central  Google Scholar 

Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P. & Green, R. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad. Pathol. 6, 2374289519873088 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Quinn, T. P., Nguyen, T., Lee, S. C. & Venkatesh, S. Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data. Front. Genet. 10, 599 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yakimovich, A., Beaugnon, A., Huang, Y. & Ozkirimli, E. Labels in a haystack: approaches beyond supervised learning in biomedical applications. Patterns 2, 100383 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Eckardt, J. N., Bornhauser, M., Wendt, K. & Middeke, J. M. Semi-supervised learning in cancer diagnostics. Front. Oncol. 12, 960984 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020).

Article  PubMed  Google Scholar 

Marini, N., Otalora, S., Muller, H. & Atzori, M. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on prostate histopathology image classification. Med. Image Anal. 73, 102165 (2021).

Article  PubMed  Google Scholar 

Doan, S., Conway, M., Phuong, T. M. & Ohno-Machado, L. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol. Biol. 1168, 275–294 (2014).

Article  PubMed  Google Scholar 

Finne, P. et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 56, 418–422 (2000).

Article  CAS  PubMed  Google Scholar 

Remzi, M. et al. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology 62, 456–460 (2003).

Article  PubMed  Google Scholar 

Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022).

Article  CAS  PubMed  Google Scholar 

Chen, A. B. et al. Artificial intelligence applications in urology: reporting standards to achieve fluency for urologists. Urol. Clin. North. Am. 49, 65–117 (2022).

Article  PubMed  Google Scholar 

Thykjaer, T. et al. Identification of gene expression patterns in superficial and invasive human bladder cancer. Cancer Res. 61, 2492–2499 (2001).

CAS  PubMed  Google Scholar 

Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822–826 (2001).

Article  CAS  PubMed  Google Scholar 

Luo, J. et al. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res. 61, 4683–4688 (2001).

CAS  PubMed  Google Scholar 

Singh, D. et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002).

Article  CAS  PubMed  Google Scholar 

Dyrskjot, L. et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat. Genet. 33, 90–96 (2003).

Article  CAS  PubMed  Google Scholar 

Blaveri, E. et al. Bladder cancer outcome and subtype classification by gene expression. Clin. Cancer Res. 11, 4044–4055 (2005).

Article  CAS  PubMed  Google Scholar 

Rhodes, D. R. et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1–6 (2004).

Article  CAS  PubMed  PubMed Central  Google Scholar 

The Cancer Genome Atlas Research Network Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).

Article  Google Scholar 

The Cancer Genome Atlas Research Network The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

Article  PubMed Central  Google Scholar 

Shen, H. et al. Integrated molecular characterization of testicular germ cell tumors. Cell Rep. 23, 3392–3406 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

Article  PubMed Central  Google Scholar 

The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2016).

Article  Google Scholar 

Veldman-Jones, M. H. et al. Evaluating robustness and sensitivity of the NanoString Technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res. 75, 2587–2593 (2015).

Article  CAS  PubMed  Google Scholar 

Zheng, H. et al. Comprehensive review of web servers and bioinformatics tools for cancer prognosis analysis. Front. Oncol. 10, 68 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

Article  PubMed  Google Scholar 

Chandrashekar, D. S. et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia 25, 18–27 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen, M. M. et al. TCPA v3.0: an integrative platform to explore the pan-cancer analysis of functional proteomic data. Mol. Cell Proteom. 18, S15–S25 (2019).

Article  CAS  Google Scholar 

Navin, N. E. The first five years of single-cell cancer genomics and beyond. Genome Res. 25, 1499–1507 (2015).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

Article  CAS  PubMed  PubMed Central