Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data

Gene expression profiling predicts clinical outcome of breast cancer

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A review on the attention mechanism of deep learning

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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

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Multimodal deep learning for biomedical data fusion: a review

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Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

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Harnessing multimodal data integration to advance precision oncology

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Critical Review of Published Microarray Studies for Cancer Outcome and Guidelines on Statistical Analysis and Reporting

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Signatures of copy number alterations in human cancer

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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

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MOLI: multi-omics late integration with deep neural networks for drug response prediction

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TCGA-Assembler: open-source software for retrieving and processing TCGA data

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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

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Deep learning for electronic health records: A comparative review of multiple deep neural architectures

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Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

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MicroRNAs and the cancer phenotype: profiling, signatures and clinical implications

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GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction

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Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

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An unsupervised machine learning method for discovering patient clusters based on genetic signatures

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Large-scale benchmark study of survival prediction methods using multi-omics data

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DNA methylation signatures for breast cancer classification and prognosis

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Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

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Controlling gene expression with deep generative design of regulatory DNA

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Semi-supervised learning improves gene expression-based prediction of cancer recurrence

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Multi-omics data integration by generative adversarial network

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Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

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On Positive-Unlabeled Classification in GAN

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SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations

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BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network

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Identifying common transcriptome signatures of cancer by interpreting deep learning models

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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

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Deep feature extraction of single-cell transcriptomes by generative adversarial network

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A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes

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EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework

Hu, Y., & Ma, W. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab272 · Cited by 20 · see citing works

Revolutionizing personalized medicine with generative AI: a systematic review

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Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications

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An Improved Lung Cancer Prediction Algorithm using Generative Adversarial Network in Modern Healthcare

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Application of deep learning-based multimodal fusion technology in cancer diagnosis: A survey

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Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review

Mishra, A., Majumder, A., Kommineni, D., Joseph, C., Chowdhury, T., & Anumula, S. K. (2025) · Cureus · doi:10.7759/cureus.82310 · Cited by 17 · see citing works

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction

Li, Z., Chen, W., Zhong, H., & Liang, C. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf124 · Cited by 16 · see citing works

Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review

Li, M., Pan, Y., Lv, Y., Ma, H., Sun, P., & Gao, H. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1516264 · Cited by 12 · see citing works

Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning

Huang, X., Huang, Y., Liu, K., Zhang, F., Zhu, Z., Xu, K., & Li, P. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00688-6 · Cited by 7 · see citing works

AI and the next medical revolution: deep learning’s uncharted healthcare promise

Krithika, L. B., Vishnu, S., Kotei, E., Gadde, A., Kothamasu, G. A., Nallabantu, S. C., & Jayabalasamy, G. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad56fc · Cited by 7 · see citing works

Integrative multi-omics and machine learning/deep learning approaches in cancer knowledge discovery: A scoping review

Benabbou, N., Abık, M., & Baichoo, S. (2026) · Cancer Treatment and Research Communications · doi:10.1016/j.ctarc.2026.101136 · Cited by 4 · see citing works

Multimodal representation learning with hierarchical knowledge decomposition for cancer survival analysis

Yang, X., & Qiu, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131053 · Cited by 2 · see citing works

Artificial intelligence in drug development: From molecular discovery to precision medicine

Xie, T., Zhang, Z., Wang, Y., Wang, H., Chen, X., Feng, H., & Shao, F. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102402 · Cited by 1 · see citing works

Domain-adaptive transfer network for visual–textual cross-domain sentiment classification

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Multi-Fusion Deep Learning with Attention Mechanisms for Renal Cancer Grade PredictionPreprint

Maqsood, F., Zhenfie, W., Hameed, K., Narejo, K. R., & Mahmood, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5248393

BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women

Lu, Z., Wang, J., Liang, Y., Lin, Z., Tan, Y., Ma, Q., Zhou, W., Zhao, Y. C., & Xu, S. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag500

Multimodal adversarial representation learning for breast cancer prognosis prediction

Du, X., & Zhao, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106765 · Cited by 21 · see citing works

GD ‐Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection

Lin, J., Deng, W., Wei, J., Zheng, J., Chen, K., Chai, H., Zeng, T., & Tang, H. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70221 · Cited by 6 · see citing works

Multimodal deep learning: An improvement in prognostication or a reflection of batch effect?

Howard, F. M., Kather, J. N., & Pearson, A. T. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.025 · Cited by 18 · see citing works

Multi-modal Cancer Survival Prediction with Attention-Based Graph Neural Networks and Embedded Feature Selection

Ji, J., Zhou, W., Chen, Y., 杨照安, Fu, Y., Yang, Y., Huang, C., & Guo, Q. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3551-3_1

Multi-modal fusion network with intra- and inter-modality attention for prognosis prediction in breast cancer

Liu, H., Shi, Y., Li, A., & Wang, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107796 · Cited by 33 · see citing works