Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

[1] Hamet P(2017)Artificial intelligence in medicine Metabolism 69 S36-S40

[2] Tremblay J(2023)Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 N Engl J Med 388 1201-1208

[3] Haug CJ(2014)Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis Theor Biol Med Model 11 1-12

[4] Drazen JM(2021)Biologically informed deep neural network for prostate cancer discovery Nature 598 348-352

[5] Li R(2021)A deep learning framework identifies pathogenic noncoding somatic mutations from personal prostate cancer genomes Cancer Res 80 4644-4654

[6] Dong X(2018)Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy Int J Radiat Oncol Biol Phys 101 128-135

[7] Ma C(2018)RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer EBioMedicine 32 234-244

[8] Liu L(2020)Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2 Genomics 112 3871-3882

[9] Elmarakeby HA(2021)Identification of Somatic Gene Signatures in Circulating Cell-Free DNA Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform Oncologist 26 751-760

[10] Wang C(2022)Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks Bioinformatics 38 3462-3469

[1] Hamet P(2017)Artificial intelligence in medicine Metabolism 69 S36-S40

[2] Tremblay J(2023)Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 N Engl J Med 388 1201-1208

[3] Haug CJ(2014)Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis Theor Biol Med Model 11 1-12

[4] Drazen JM(2021)Biologically informed deep neural network for prostate cancer discovery Nature 598 348-352

[5] Li R(2021)A deep learning framework identifies pathogenic noncoding somatic mutations from personal prostate cancer genomes Cancer Res 80 4644-4654

[6] Dong X(2018)Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy Int J Radiat Oncol Biol Phys 101 128-135

[7] Ma C(2018)RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer EBioMedicine 32 234-244

[8] Liu L(2020)Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2 Genomics 112 3871-3882

[9] Elmarakeby HA(2021)Identification of Somatic Gene Signatures in Circulating Cell-Free DNA Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform Oncologist 26 751-760

[10] Wang C(2022)Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks Bioinformatics 38 3462-3469