Biologically informed deep neural network for prostate cancer discovery
Open accessPublished version of a preprint
Abstract
Abstract The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge 1,2 . Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics 3–5 . Here we developed P-NET—a biologically informed deep learning model—to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1 , which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
P-NET applied hierarchical pathway information to construct a sparse deep neural network for inferring disease status-specific molecular alterations, aiding disease diagnosis and drug design for prostate cancer.
“P-NET applied the hierarchical pathway information to construct a sparse deep neural network for inferring the disease status-specific molecular alterations, which aids in disease diagnosis and drug design for prostate cancer [116].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [116]
The Results and Methods (P-NET design) describe a sparse neural network built from hierarchical pathways to predict prostate cancer state from genomic profiles. The Discussion says it identified molecular candidates and potential therapeutic opportunities, but does not report drug design; it also frames clinical prediction as a potential application rather than an established diagnostic use. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMultiple deep learning methods have been developed to integrate biological pathway information with neural network architectures, but these methods are still limited by predefined pathway annotations.
“While multiple deep learning methods have been developed to integrate biological pathway information with neural network architectures [116,166], these methods are still limited by the predefined pathway annotations.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [116]
The Main section describes several biologically informed neural networks, and the Discussion says P-NET depends on the quality of its hardcoded pathway annotations. The paper does not establish that all the methods mentioned share this limitation; that broader claim goes beyond its stated finding. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe common approach for biological model design is to construct a neural network architecture restrained by biological pathways, connecting target gene nodes with pathway nodes, including P-NET, pmVAE, VEGA, and LDVAE.
“The common approach for biological model design is to construct a neural network architecture that is restrained by biological pathways, specifically connecting target gene nodes with pathway nodes, including models of P-NET [116], pmVAE [163], VEGA [164], and LDVAE [165].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [116]
P-NET is a sparse deep learning model that processes molecular profiling data in a pathway-driven fashion to predict prostate disease states such as metastases, and its prediction scores independently correlate with prognosis.
“P-NET (112) is a sparse deep learning model that processes molecular profiling data in a biologically informed, pathway-driven fashion to predict prostate disease states such as metastases, where the resulting prediction scores were shown to independently correlate with prognosis.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [112]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889 · see citing works
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Integrative Clinical Genomics of Advanced Prostate Cancer
Robinson, D., Van Allen, E. M., Wu, Y.-M., Schultz, N., Lonigro, R. J., Mosquera, J.-M., Montgomery, B., Taplin, M.-E., Pritchard, C. C., Attard, G., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.001 · Cited by 3,645 · see citing works
Mermel, C. H., Schumacher, S. E., Hill, B., Meyerson, M. M., Beroukhim, R., & Getz, G. A. (2011) · Genome biology · doi:10.1186/gb-2011-12-4-r41 · Cited by 3,469 · see citing works
Discovery and saturation analysis of cancer genes across 21 tumour types
Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096 · see citing works
Pruning Filters for Efficient ConvNets
Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2016) · arXiv · doi:10.48550/arxiv.1608.08710 · Cited by 2,928 · see citing works
The Reactome Pathway Knowledgebase
Fabregat, A., Jupe, S., Matthews, L. R., Sidiropoulos, K., Gillespie, M. E., Garapati, P. V., Haw, R., Jassal, B., Korninger, F., May, B., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1132 · Cited by 2,798 · see citing works
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
UpSet: Visualization of Intersecting Sets
Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346248 · Cited by 2,340 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers
Sondka, Z., Bamford, S., Cole, C. G., Ward, S. A., Dunham, I., & Forbes, S. A. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0060-1 · Cited by 1,597 · see citing works
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J., & Carbin, M. (2018) · arXiv · doi:10.48550/arxiv.1803.03635 · Cited by 1,529 · see citing works
Genomic correlates of clinical outcome in advanced prostate cancer
Abida, W., Cyrta, J., Heller, G., Prandi, D., Armenia, J., Coleman, I., Cieslik, M., Benelli, M., Robinson, D., Van Allen, E. M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902651116 · Cited by 1,372 · see citing works
The evolutionary history of lethal metastatic prostate cancer
ICGC Prostate UK Group, Gundem, G., Loo, P. V., Kremeyer, B., Alexandrov, L. B., Tubío, J. M. C., Papaemmanuil, E., Brewer, D. S., Kallio, H. M. L., Högnäs, G., et al. (2015) · Nature · doi:10.1038/nature14347 · Cited by 1,358 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
The long tail of oncogenic drivers in prostate cancer
PCF/SU2C International Prostate Cancer Dream Team, Armenia, J., Wankowicz, S. A. M., Liu, D., Gao, J., Kundra, R., Reznik, E., Chatila, W. K., Chakravarty, D., Han, G. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0078-z · Cited by 873 · see citing works
Self-citationIntegrative clinical genomics of metastatic cancer
Robinson, D. R., Wu, Y.-M., Lonigro, R. J., Vats, P., Cobain, E., Everett, J., Cao, X., Rabban, E., Kumar-Sinha, C., Raymond, V., et al. (2017) · Nature · doi:10.1038/nature23306 · Cited by 863 · see citing works
p53: 800 million years of evolution and 40 years of discovery
Levine, A. J. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0262-1 · Cited by 850 · see citing works
Aggarwal, R., Huang, J., Alumkal, J. J., Zhang, L., Feng, F. Y., Thomas, G. V., Weinstein, A. S., Friedl, V., Zhang, C., Witte, O. N., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2017.77.6880 · Cited by 810 · see citing works
Androgen Receptor Pathway-Independent Prostate Cancer Is Sustained through FGF Signaling
Bluemn, E. G., Coleman, I. M., Lucas, J. M., Coleman, R. T., Hernandez-Lopez, S., Tharakan, R., Bianchi-Frias, D., Dumpit, R. F., Kaipainen, A., Corella, A. N., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.09.003 · Cited by 744 · see citing works
Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer
Quigley, D. A., Dang, H. X., Zhao, S. G., Lloyd, P., Aggarwal, R., Alumkal, J. J., Foye, A., Kothari, V., Perry, M. D., Bailey, A. M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.039 · Cited by 733 · see citing works
Haas, B. J., Dobin, A., Li, B., Stransky, N., Pochet, N., & Regev, A. (2019) · Genome biology · doi:10.1186/s13059-019-1842-9 · Cited by 646 · see citing works
Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000249929 · Cited by 634 · see citing works
Genomic hallmarks of localized, non-indolent prostate cancer
Fraser, M. E., Sabelnykova, V. Y., Yamaguchi, T. N., Heisler, L. E., Livingstone, J. M., Huang, V., Shiah, Y.-J., Yousif, F., Lin, X., Masella, A. P., et al. (2017) · Nature · doi:10.1038/nature20788 · Cited by 589 · see citing works
Integrative Clinical Genomics of Advanced Prostate Cancer
Robinson, D. R., Allen, E. M. V., Wu, Y., Schultz, N. D., Lonigro, R. J., Mosquera, J., Montgomery, B., Taplin, M., Pritchard, C. C., Attard, G., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.053 · Cited by 570 · see citing works
Predicting drug response and synergy using a deep learning model of human cancer cells
Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.014 · Cited by 544 · see citing works
Self-citationp53 Protein Accumulation and Gene Mutation in the Progression of Human Prostate Carcinoma
Navone, N. M., Troncoso, P., Pisters, L. L., Goodrow, T. L., Palmer, J. L., Nichols, W. W., von Eschenbach, A. C., & Conti, C. J. (1993) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/85.20.1657 · Cited by 477 · see citing works
Using deep learning to model the hierarchical structure and function of a cell
Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods · doi:10.1038/nmeth.4627 · Cited by 435 · see citing works
Copy number alteration burden predicts prostate cancer relapse
Hieronymus, H., Schultz, N. D., Gopalan, A., Carver, B. S., Chang, M. T., Xiao, Y., Heguy, A., Huberman, K. H., Bernstein, M. L., Assel, M. J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1411446111 · Cited by 364 · see citing works
The DNA methylation landscape of advanced prostate cancer
Zhao, S. G., Chen, W. S., Li, H., Foye, A., Zhang, M., Sjöström, M., Aggarwal, R. R., Playdle, D., Liao, A., Alumkal, J. J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0648-8 · Cited by 351 · see citing works
Sharma, A. M., Yeow, W.-S., Ertel, A., Coleman, I. M., Clegg, N. J., Thangavel, C., Morrissey, C. M., Zhang, X., Comstock, C. E. S., Witkiewicz, A. K., et al. (2010) · Journal of Clinical Investigation · doi:10.1172/jci44239 · Cited by 327 · see citing works
A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action
Yang, J. H., Wright, S. N., Hamblin, M., McCloskey, D., Alcantar, M. A., Schrübbers, L. C., Lopatkin, A. J., Satish, S., Nili, A., Palsson, B. Ø., et al. (2019) · Cell · doi:10.1016/j.cell.2019.04.016 · Cited by 317 · see citing works
Activation of the p53 pathway by small-molecule-induced MDM2 and MDMX dimerization
Graves, B., Thompson, T., Xia, M., Janson, C., Lukacs, C., Deo, D., Di Lello, P., Fry, D., Garvie, C., Huang, K.-S., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203789109 · Cited by 230 · see citing works
Sparse Networks from Scratch: Faster Training without Losing PerformancePreprint
Dettmers, T., & Zettlemoyer, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04840 · Cited by 230 · see citing works
Rodrigues, D. N., Rescigno, P., Liu, D., Yuan, W., Carreira, S., Lambros, M. B. K., Seed, G., Mateo, J., Riisnaes, R., Mullane, S. A., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci121924 · Cited by 204 · see citing works
Self-citationClinical significance of p53 alterations in surgically treated prostate cancers
Schlomm, T., Iwers, L., Kirstein, P., Jessen, B., Köllermann, J., Minner, S., Passow-Drolet, A., Mirlacher, M., Milde-Langosch, K., Graefen, M., et al. (2008) · Modern Pathology · doi:10.1038/modpathol.2008.104 · Cited by 196 · see citing works
Zhang, Z., Li, M., Wang, H., Agrawal, S., & Zhang, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1934692100 · Cited by 192 · see citing works
Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs
Xu, Q., Zhang, M., Gu, Z., & Pan, G. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.080 · Cited by 176 · see citing works
Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer
Quigley, D. A., Dang, H. X., Zhao, S. G., Lloyd, P., Aggarwal, R. R., Alumkal, J. J., Foye, A., Kothari, V., Perry, M. D., Bailey, A. M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.019 · Cited by 171 · see citing works
Regulation of p53: a collaboration between Mdm2 and MdmX
Pei, D., Zhang, Y., & Zheng, J. (2012) · Oncotarget · doi:10.18632/oncotarget.443 · Cited by 140 · see citing works
Hao, J., Kim, Y. S., Kim, T.-K., & Kang, M. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2500-z · Cited by 139 · see citing works
Chen, W. S., Aggarwal, R. R., Zhang, L., Zhao, S. G., THOMAS, G. V., Beer, T. M., Quigley, D. A., Foye, A., Playdle, D., Huang, J., et al. (2019) · European Urology · doi:10.1016/j.eururo.2019.03.020 · Cited by 130 · see citing works
Abnormal Expression of MDM2 in Prostate Carcinoma
Leite, K. R. M., de Franco, M. F., Srougi, M., Nesrallah, L., Nesrallah, A., Bevilacqua, R. G., Darini, E., Carvalho, C. M., Meirelles, M. I., Santana, I., et al. (2001) · Modern Pathology · doi:10.1038/modpathol.3880330 · Cited by 102 · see citing works
MDM2 and MDM4 Are Therapeutic Vulnerabilities in Malignant Rhabdoid Tumors
Howard, T. P., Arnoff, T. E., Song, M. R., Giacomelli, A. O., Wang, X., Hong, A. L., Dharia, N. V., Wang, S., Vázquez, F., Pham, M., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3066 · Cited by 70 · see citing works
Self-citationSutinen, P., Malinen, M., Heikkinen, S., & Palvimo, J. J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku543 · Cited by 70 · see citing works
Hwang, J. H., Seo, J.-H., Beshiri, M. L., Wankowicz, S. A. M., Liu, D., Cheung, A. T. M., Li, J., Qiu, X., Hong, A. L., Botta, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.068 · Cited by 67 · see citing works
Self-citationFarah, E., Li, C., Cheng, L., Kong, Y., Lanman, N. A., Pascuzzi, P. E., Lorenz, G. R., Zhang, Y., Ahmad, N., Li, L., et al. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.006983 · Cited by 67 · see citing works
Role of Runx2 phosphorylation in prostate cancer and association with metastatic disease
Ge, C., Zhao, G., Li, Y., Li, H., Zhao, X., Pannone, G., Bufo, P., Santoro, A., Sanguedolce, F., Tortorella, S., et al. (2015) · Oncogene · doi:10.1038/onc.2015.91 · Cited by 64 · see citing works
Feng, F. Y.-C., Zhang, Y., Kothari, V., Evans, J. R., Jackson, W., Chen, W., Johnson, S. B., Luczak, C., Wang, S., & Hamstra, D. A. (2016) · Neoplasia · doi:10.1016/j.neo.2016.01.006 · Cited by 59 · see citing works
Rodrigues, D. N., Rescigno, P., Liu, D., Yuan, W., Carreira, S., Lambros, M. B. K., Seed, G., Mateo, J., Riisnaes, R., Mullane, S. A., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci125184 · Cited by 53 · see citing works
Self-citationRoles of Ubiquitination and SUMOylation on Prostate Cancer: Mechanisms and Clinical Implications
Chen, Z., & Lu, W. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16034560 · Cited by 51 · see citing works
Tovar, C., Higgins, B., Kolinsky, K. D., Xia, M., Packman, K. E., Heimbrook, D. C., & Vassilev, L. T. (2011) · Molecular Cancer · doi:10.1186/1476-4598-10-49 · Cited by 40 · see citing works
Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., et al. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.1711.06104 · Cited by 26 · see citing works
Chopra, H., Khan, Z. A., Contreras, J., Wang, H., Sedrak, A., & Zhu, Y. Y. (2017) · Oncotarget · doi:10.18632/oncotarget.23569 · Cited by 25 · see citing works
Han, G. C., Hwang, J. H., Wankowicz, S. A. M., Zhang, Z., Liu, D., Cibulskis, C., Gaviola, G. C., Ghazikhanian, V., McKay, R. R., Bubley, G. J., et al. (2017) · JCO Precision Oncology · doi:10.1200/po.17.00140 · Cited by 24 · see citing works
Self-citationChen, W. S., Aggarwal, R. R., Zhang, L., Zhao, S. G., Beer, T. M., Quigley, D. A., Foye, A., Playdle, D., Lloyd, P., Rettig, M. B., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.2019.37.7_suppl.146 · Cited by 17 · see citing works
Flagged first, then by citations
2 of the 61 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [16]Han S., Pool J., Tran J., Dally W. in Advances in Neural Information Processing Systems (eds Cortes C. et al.) 1135–1143 (Curran Associates, 2015).
- [22]Lundberg S. M., Lee S.-I. in Advances in Neural Information Processing Systems 30 (eds Guyon I. et al.) 4765–4774 (Curran Associates, 2017).
Cited by
Sorted by citations
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Obtaining genetics insights from deep learning via explainable artificial intelligence
Novakovsky, G., Dexter, N., Libbrecht, M. W., Wasserman, W. W., & Mostafavi, S. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00532-2 · Cited by 387 · see citing works
Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Advancing Computational Toxicology by Interpretable Machine Learning
Jia, X., Wang, T., & Zhu, H. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00653 · Cited by 193 · see citing works
Jaume, G., Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Liang, P. P., & Mahmood, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01100 · Cited by 161 · see citing works
Yoo, S., Fitzgerald, C. W. R., Cho, B. A., Fitzgerald, B. G., Han, C. Y., Koh, E. S., Pandey, A., Sfreddo, H. J., Crowley, F., Rudshteyn, M., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03398-5 · Cited by 136 · see citing works
Wu, C., Lorenzo, G., Hormuth, D. A., Lima, E. A. B. F., Slavkova, K. P., DiCarlo, J. C., Virostko, J. M., Phillips, C. M., Patt, D. A., Chung, C. D., et al. (2022) · Biophysics Reviews · doi:10.1063/5.0086789 · Cited by 138 · see citing works
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works
Deep learning in cancer genomics and histopathology
Unger, M., & Kather, J. N. (2024) · Genome Medicine · doi:10.1186/s13073-024-01315-6 · Cited by 124 · see citing works
Current and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 118 · see citing works
Chen, V., Yang, M., Cui, W., Kim, J. S., Talwalkar, A., & Ma, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02359-7 · Cited by 133 · see citing works
Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots
Su, J., He, K., Li, Y., Tu, J., & Chen, X. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00906 · Cited by 118 · see citing works
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
Wang, H., Zhan, Z., Hu, F., Meng, Y., Liu, Z., Fu, X., & Liu, Q. (2023) · PhotoniX · doi:10.1186/s43074-022-00079-9 · Cited by 101 · see citing works
Drug resistance in cancer: molecular mechanisms and emerging treatment strategies
Li, J., Hu, J., Yang, Y., Zhang, H., Liu, Y., Fang, Y., Qu, L., Lin, A., Luo, P., Jiang, A., et al. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00352-w · Cited by 101 · see citing works
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Liang, J., Zhang, W., Yang, J., Wu, M., Dai, Q., Yin, H., Xiao, Y., & Kong, L. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00635-3 · Cited by 97 · see citing works
Immune digital twins for complex human pathologies: applications, limitations, and challenges
Niarakis, A., Laubenbacher, R., An, G. C., Ilan, Y., Fisher, J., Flobak, Å., Reiche, K., Martínez, M. R., Geris, L., Ladeira, L., et al. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00450-5 · Cited by 104 · see citing works
Hartman, E., Scott, A. M., Karlsson, C. A. Q., Mohanty, T., Vaara, S. T., Linder, A., Malmström, L., & Malmström, J. A. (2023) · Nature Communications · doi:10.1038/s41467-023-41146-4 · Cited by 97 · see citing works
An overview and a roadmap for artificial intelligence in hematology and oncology
Rösler, W., Altenbuchinger, M., Baeßler, B., Beißbarth, T., Beutel, G., Bock, R., von Bubnoff, N. C. C., Eckardt, J., Foersch, S., Loeffler, C. M. L., et al. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04667-5 · Cited by 91 · see citing works
Moon, S., & Lee, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac080 · Cited by 86 · see citing works
Yu, F., & Hu, X. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.128730 · Cited by 80 · see citing works
Wysocka, M., Wysocki, O., Zufferey, M., Landers, D., & Freitas, A. (2022) · BMC Bioinformatics · doi:10.1186/s12859-023-05262-8 · Cited by 75 · see citing works
Sethi, B. K., Singh, D., Rout, S. K., & Panda, S. K. (2023) · IEEE Access · doi:10.1109/access.2023.3346925 · Cited by 72 · see citing works
A review of mechanistic learning in mathematical oncology
Metzcar, J., Jutzeler, C. R., Macklin, P., Köhn‐Luque, A., & Brüningk, S. C. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1363144 · Cited by 74 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
Abdollahi, H., Yousefirizi, F., Shiri, I., Brosch-Lenz, J., Mollaheydar, E., Fele‐Paranj, A., Shi, K., Zaidi, H., Alberts, I., Soltani, M., et al. (2024) · Theranostics · doi:10.7150/thno.93973 · Cited by 68 · see citing works
Revealing hidden patterns in deep neural network feature space continuum via manifold learning
Islam, M. T., Zhou, Z., Ren, H., Khuzani, M. B., Kapp, D. S., Zou, J., Tian, L., Liao, J. C., & Xing, L. (2023) · Nature Communications · doi:10.1038/s41467-023-43958-w · Cited by 65 · see citing works
Noordijk, B., García‐Gómez, M. L., ten Tusscher, K., de Ridder, D., van Dijk, A. D. J., & Smith, R. W. (2024) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1407994 · Cited by 64 · see citing works
Artificial intelligence-based biomarkers for treatment decisions in oncology
Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 70 · see citing works
Park, S., Silva, E., Singhal, A., Kelly, M. R., Licon, K. S., Panagiotou, I., Fogg, C., Fong, S. H., Lee, J. J. Y., Zhao, X., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00740-1 · Cited by 60 · see citing works
Ouyang, B., Shan, C., Shen, S., Dai, X., Chen, Q., Su, X., Cao, Y., Qin, X., He, Y., Wang, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51980-9 · Cited by 56 · see citing works
Andreev, A. V., Kurkin, S., Stoyanov, D., Badarin, A., Paunova, R., & Hramov, A. (2023) · Chaos · doi:10.1063/5.0155567 · Cited by 53 · see citing works
π-HuB: the proteomic navigator of the human body
He, F., Aebersold, R. H., Baker, M. S., Bian, X., Bo, X., Chan, D. W., Chang, C., Chen, L., Chen, X., Chen, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-08280-5 · Cited by 58 · see citing works
New horizons at the interface of artificial intelligence and translational cancer research
Yates, J., & Allen, E. M. V. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.018 · Cited by 55 · see citing works
Pandiyan, S., & Wang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106140 · Cited by 60 · see citing works
He, M., Cao, Y., Chi, C., Yang, X., Rzayev, R. T., Wang, S., Yang, G., Mukhtorov, O. S., Zhang, L., Kazantsev, A. N., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1189370 · Cited by 57 · see citing works
Schaduangrat, N., Anuwongcharoen, N., Charoenkwan, P., & Shoombuatong, W. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00721-z · Cited by 52 · see citing works
Zhang, T., Hasib, M. M., Chiu, Y., Han, Z.-F., Jin, Y.-F., Flores, M. A., Chen, Y., & Huang, Y. (2022) · Cancers · doi:10.3390/cancers14194763 · Cited by 55 · see citing works
Deep learning and transfer learning for device-free human activity recognition: A survey
Yang, J., Xu, Y., Cao, H., Zou, H., & Xie, L. (2022) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2022.100007 · Cited by 54 · see citing works
Rai, H. M., Yoo, J., & Razaque, A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124838 · Cited by 54 · see citing works
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
Qin, T., Yang, H., Wang, L., Xue, W., Yao, N., Li, Q., Chen, X., Yang, X., Yu, X., Zhang, Q., et al. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202408902 · Cited by 49 · see citing works
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 57 · see citing works
Liu, X. F., Tao, Y., Cai, Z., Bao, P., Ma, H., Li, K., Li, M., Zhu, Y., & Lu, Z. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae316 · Cited by 53 · see citing works
Anh, N. K., Thu, N. Q., Tien, N. T. N., Long, N. P., & Nguyen, H. T. (2024) · Molecules · doi:10.3390/molecules29245934 · Cited by 49 · see citing works
Explainable Multilayer Graph Neural Network for cancer gene prediction
Chatzianastasis, M., Vazirgiannis, M., & Zhang, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad643 · Cited by 49 · see citing works
Riaz, I. B., Harmon, S. A., Chen, Z., Naqvi, S. A. A., & Liang Cheng, C. (2024) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_438516 · Cited by 49 · see citing works
Machine learning applications for therapeutic tasks with genomics data
Huang, K., Xiao, C., Glass, L. M., Critchlow, C. W., Gibson, G. C., & Sun, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100328 · Cited by 46 · see citing works
Future directions for precision oncology in prostate cancer
Mizuno, K., & Beltran, H. (2022) · The Prostate · doi:10.1002/pros.24354 · Cited by 46 · see citing works
Multi-omics based artificial intelligence for cancer research
Li, L., Sun, M., Wang, J., & Wan, S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.005 · Cited by 47 · see citing works
Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer
Wei, Z., Han, D., Zhang, C., Wang, S., Liu, J., Chao, F., Song, Z., & Chen, G. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.893424 · Cited by 45 · see citing works
Liu, F., Beck, S., Yang, L., Luo, H., & Zhang, K. C. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00922-2 · Cited by 52 · see citing works
Biologically informed deep learning for explainable epigenetic clocks
Prósz, A. G., Pipek, O., Börcsök, J., Palla, G., Szállási, Z., Spisák, S., & Csabai, I. (2024) · Scientific Reports · doi:10.1038/s41598-023-50495-5 · Cited by 44 · see citing works
Yan, H. H., Weng, D., Li, D., Gu, Y., Ma, W., & Liu, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae184 · Cited by 44 · see citing works
E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring
Sun, X., Guo, X., Gao, J., Wu, J., Huang, F., Zhang, J., Huang, F., Lu, X. J., Shi, Y., & Pan, L. (2024) · Biomedicines · doi:10.3390/biomedicines12102307 · Cited by 43 · see citing works
Knowledge-guided deep learning models of drug toxicity improve interpretation
Hao, Y., Romano, J. D., & Moore, J. H. (2022) · Patterns · doi:10.1016/j.patter.2022.100565 · Cited by 40 · see citing works
MUMA: A Multi-Omics Meta-Learning Algorithm for Data Interpretation and Classification
Huang, H., Shu, J., & Liang, Y. Q. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3363081 · Cited by 40 · see citing works
Zhang, L., Li, W., Parvin, R., Wang, X., Fan, Q., & Ye, F. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202207879 · Cited by 39 · see citing works
Leveraging deep learning to improve vaccine design
Hederman, A. P., & Ackerman, M. E. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.03.002 · Cited by 40 · see citing works
Zhang, Z., & Wei, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.005 · Cited by 41 · see citing works
Yang, J., Li, Z., Wu, W. K. K., Shi, Y., Xu, Z., Chu, Q., & Zhang, Q. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac469 · Cited by 38 · see citing works
A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer
Baptista, D., Ferreira, P. G., & Rocha, M. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010200 · Cited by 38 · see citing works
Designing interpretable deep learning applications for functional genomics: a quantitative analysis
van Hilten, A., Katz, S., Saccenti, E., Niessen, W. J., & Roshchupkin, G. V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae449 · Cited by 39 · see citing works
Zhong, S., Su, L., Xu, M., Loke, D. K., Yu, B., Zhang, Y., & Zhao, R. J. (2024) · Nano-Micro Letters · doi:10.1007/s40820-024-01550-x · Cited by 38 · see citing works
Graph Artificial Intelligence in Medicine
Johnson, R., Li, M. M., Noori, A., Queen, O., & Žitnik, M. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-110723-024625 · Cited by 40 · see citing works
Fu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 36 · see citing works
Wu, T., Zhao, L., Ren, M., He, S., Zhang, L., Fang, M., & Wang, B. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c11832 · Cited by 35 · see citing works
Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression
Alshareef, A. M., Alsini, R. A. I., Alsieni, M., Alrowais, F., Marzouk, R., Abunadi, I., & Nemri, N. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/7364704 · Cited by 35 · see citing works
Learning discriminative and structural samples for rare cell types with deep generative model
Wang, H., & Ma, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac317 · Cited by 34 · see citing works
Zhao, X., Singhal, A., Park, S., Kong, J., Bachelder, R. E., & Ideker, T. E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0641 · Cited by 33 · see citing works
Zelli, V., Manno, A., Compagnoni, C., Ibraheem, R., Zazzeroni, F., Alesse, E., Rossi, F., Arbib, C., & Tessitore, A. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04720-4 · Cited by 32 · see citing works
Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies.
Liu, W., Chen, J.-W., Wang, H.-B., Fu, Z.-Q., Peijnenburg, W., & Hong, H.-X. (2024) · Environmental Science and Technology · doi:10.1021/acs.est.4c03088 · Cited by 44 · see citing works
A review on deep learning applications in highly multiplexed tissue imaging data analysis
Zidane, M., Makky, A., Bruhns, M., Rochwarger, A., Babaei, S., Claassen, M., & Schürch, C. M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1159381 · Cited by 30 · see citing works
DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways
Cai, Z., Poulos, R. C., Aref, A., Robinson, P. J., Reddel, R. R., & Zhong, Q. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-24-0285 · Cited by 36 · see citing works
Wang, H., Lin, K., Zhang, Q., Shi, J., Song, X., Wu, J., Zhao, C., & He, K. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae159 · Cited by 30 · see citing works
A spectrum of explainable and interpretable machine learning approaches for genomic studies
Conard, A. M., DenAdel, A., & Crawford, L. A. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1617 · Cited by 30 · see citing works
van Hilten, A., van Rooij, J., Heijmans, B. T., Hoen, P. A. C. ’., van Meurs, J. B. J., Jansen, R., Franke, L., Boomsma, D. I., Pool, R., van Dongen, J., et al. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00405-w · Cited by 27 · see citing works
Huang, X., Huang, K., Johnson, T. S., Radovich, M., Zhang, J., Ma, J., & Wang, Y. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab097 · Cited by 27 · see citing works
Integration of epigenetic regulatory mechanisms in heart failure
Sopić, M., Robinson, E. L., Emanueli, C., Srivastava, P. K., Angione, C., Gaetano, C., Condorelli, G., Martelli, F., Pedrazzini, T., Devaux, Y., et al. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-00986-3 · Cited by 28 · see citing works
Lan, W., Tang, Z., Liao, H., Chen, Q., Chen, Y. P., Zhang, Z., & Wang, J. (2025) · Fundamental Research · doi:10.1016/j.fmre.2025.01.004 · Cited by 26 · see citing works
Hwang, J. H., Arafeh, R., Seo, J.-H., Baca, S. C., Ludwig, M., Arnoff, T. E., Sawyer, L., Richter, C., Tape, S., Bergom, H. E., et al. (2022) · eLife · doi:10.7554/elife.73223 · Cited by 26 · see citing works
Self-citationImproving plant breeding through AI-supported data integration
Sangjan, W., Kick, D. R., & Washburn, J. D. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-04910-2 · Cited by 24 · see citing works
Aref‐Eshghi, E., Abadi, A., Farhadieh, M., Hooshmand, A., Ghasemi, F., Youssefian, L., Vahidnezhad, H., Kerrins, T. M., Zhao, X., Akbarzadeh, M., et al. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01967-0 · Cited by 25 · see citing works
The Rise of Hypothesis-Driven Artificial Intelligence in Oncology
Xianyu, Z., Correia, C., Ung, C. Y., Zhu, S., Billadeau, D. D., & Li, H. (2024) · Cancers · doi:10.3390/cancers16040822 · Cited by 25 · see citing works
Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy
Azadinejad, H., Rad, M. F., Shariftabrizi, A., Rahmim, A., & Abdollahi, H. (2025) · Diagnostics · doi:10.3390/diagnostics15030397 · Cited by 23 · see citing works
Target identification and assessment in the era of AI
Pun, F. W., Podolskiy, D. I., Izumchenko, E., Mortlock, A. A., Oprea, T. I., Scheibye‐Knudsen, M., Fortney, K., Morgen, E. K., Ren, F., & Zhavoronkov, A. A. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01412-8 · Cited by 28 · see citing works
Yang, C., Liu, Z., Fang, Y., Cao, X., Xu, G., Wang, Z., Hu, Z., Wang, S., & Wu, X. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04318-w · Cited by 23 · see citing works
Pang, J., Liang, B., Ding, R., Yan, Q., Chen, R., & Xu, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad304 · Cited by 23 · see citing works
Stochastic neuro-fuzzy system implemented in memristor crossbar arrays
Shi, T., Zhang, H., Cui, S., Liu, J., Gu, Z., Wang, Z., Yan, X., & Liu, Q. (2024) · Science Advances · doi:10.1126/sciadv.adl3135 · Cited by 22 · see citing works
Artificial intelligence-aided data mining of medical records for cancer detection and screening
Haue, A. D., Hjaltelin, J. X., Holm, P. C., Placido, D., & Brunak, S. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00277-8 · Cited by 23 · see citing works
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review
Mao, L., Wang, H., Hu, L., Tran, N. L., Canoll, P., Swanson, K. R., & Li, J. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3515839 · Cited by 24 · see citing works
Bourgeais, V., Zehraoui, F., & Hanczar, B. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac147 · Cited by 22 · see citing works
PAUSE: principled feature attribution for unsupervised gene expression analysis
Janizek, J. D., Spiro, A. E., Çelik, S., Blue, B. W., RUSSELL, J. C., Lee, T.-I., Kaeberlin, M., & Lee, S. (2023) · Genome biology · doi:10.1186/s13059-023-02901-4 · Cited by 21 · see citing works
Ren, S., Li, J., Dorado, J., Sierra, A., González‐Díaz, H., Duardo-Sánchez, A., & Shen, B. (2023) · Health Information Science and Systems · doi:10.1007/s13755-023-00264-5 · Cited by 21 · see citing works
Yang, C., Li, B., Luan, Y., Wang, S., Bian, Y., Zhang, J., Wang, Z., Liu, B., Chen, X., Hacker, M., et al. (2024) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2024.01.021 · Cited by 21 · see citing works
Launonen, K., Varis, V., Aaltonen, N., Niskanen, E. A., Varjosalo, M. T., Paakinaho, V., & Palvimo, J. J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae653 · Cited by 21 · see citing works
AI Model for Prostate Biopsies Predicts Cancer Survival
Sandeman, K., Blom, S., Koponen, V., Manninen, A., Juhila, J., Rannikko, A. S., Ropponen, T., & Mirtti, T. K. (2022) · Diagnostics · doi:10.3390/diagnostics12051031 · Cited by 21 · see citing works
A Survey on Human Cancer Categorization Based on Deep Learning
Ibrahim, A., Mohamed, H. K., Maher, A., & Zhang, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.884749 · Cited by 21 · see citing works
- Authors
- Elmarakeby, H. A., Hwang, J., Arafeh, R., Crowdis, J., Gang, S., Liu, D., AlDubayan, S. H., Salari, K., Kregel, S., Richter, C., Arnoff, T. E., Park, J., Hahn, W. C., & Van Allen, E. M.
- Affiliations
- Broad Institute (US)
- University of Minnesota (US)
- Dana-Farber Cancer Institute (US)
- University of Illinois Chicago (US)
- ORCID
- Venue
- Nature
- Published
- 2021-09-22
- Volume
- 598
- Issue
- 7880
- Pages
- 348-352
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8514339
- OpenAlex
- W3199772305
- Cited by
- 456
- References
- 61
- Citations by year
- 2026: 99 · 2025: 105 · 2024: 111 · 2023: 88 · 2022: 47 · 2021: 5 · 2020: 1
- FWCI
- 41.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Defense (U01 CA233100, PC200150)
- Movember Foundation
- National Institutes of Health
- National Cancer Institute (U01CA176058, U01CA233100)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Treatment and Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- prostate cancer, treatment resistance, interpretable neural networks, MDM4, FGFR1, cancer genomics, patient stratification, therapeutic targeting
- MeSH terms
- Deep Learning, Humans, Male, Prostatic Neoplasms, Proto-Oncogene Proteins, Receptors, Androgen, Reproducibility of Results, Tumor Suppressor Protein p53, Cell Cycle Proteins, Drug Resistance, Neoplasm, Receptor, Fibroblast Growth Factor, Type 1
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
