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Multimodal data fusion for cancer biomarker discovery with deep learning
Open accessAbstract
Technological advances have made it possible to study a patient from multiple angles with high-dimensional, high-throughput multiscale biomedical data. In oncology, massive amounts of data are being generated, ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has greatly advanced the analysis of biomedical data. However, most approaches focus on single data modalities, leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalized medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability and standardization of datasets. Cancer diagnosis and treatment decisions often focus on one data source. Steyaert and colleagues discuss the current status and challenges of data fusion, including electronic health records, molecular data, digital pathology and radiographic images, in cancer research and translational development.
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“NLU is central to enhancing the comprehension and contextualization of human language in AI systems, and involves key capabilities such as semantic analysis, named entity recognition, sentiment analysis, textual entailment, and machine reading comprehension [190], [191], [192], [193].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [192]
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“AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human clinical decision-making.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [14]
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“This can boost their robustness and accuracy, enable the discovery of new biomarkers and therapeutic targets6,14, as well as improve model performance15–17.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [14]
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“The understanding of important gene features or cross-modality regulatory networks is essential to uncover the mechanism of disease development and identify new disease biomarkers and drug targets [28].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [28]
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“The high costs of biomedical data generation, difficulties in patient follow-up, and privacy restrictions limit the size of training datasets and lead to missing data modalities [28].”
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“The data generated by these diverse modalities is generally complementary, with each modality contributing unique information to the status of a patient.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Steyaert et al., 2023
In the citing paper
“Advances in NLU have been crucial in improving AI's proficiency in interpreting and analyzing language across a spectrum of contexts, ranging from straightforward conversational exchanges to intricate textual data [190], [192], [193].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [192]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1×
“Together, these efforts have the shared aim to process, analyse and share data using a community-embraced standard in a FAIR (findable, accessible, interoperable and reusable) way 156 .”
BackgroundThe 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella‐Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,703
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Circos: An information aesthetic for comparative genomicsPreprint
Krzywinski, M. I., Schein, J. E., Birol, İ., Connors, J. M., Gascoyne, R. D., Horsman, D. E., Jones, S. J. M., & Marra, M. A. (2009) · Genome Research · doi:10.1101/gr.092759.109 · Cited by 11,073
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Hallmarks of Cancer: New Dimensions
Hanahan, D. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1059 · Cited by 9,887
Research works printed but not linked: 4
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G. & Dean, J. Distributed representations of words and phrases and their compositionality. In Proc. 26th International Conference on Neural Information Processing Systems 3111–3119 (Curran Associates, Inc., 2013).
- Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 4171–4186 (Association for Computational Linguistics, 2019).
- Jie, Y., Xutong, L. & Mingyue, Z. Current status of active learning for drug discovery. Artif. Intell. Life Sci. 1, 100023 (2021).
- Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
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Deep learning based multimodal biomedical data fusion: an overview and comparative review
Duan, J., Xiong, J., Li, Y., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102536 · Cited by 284
Shaik, T., Tao, X., Li, L., Xie, H., & Velasquez, J. D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102040 · Cited by 228
Cembrowska-Lech, D., Krzemińska, A., Miller, T. I., Nowakowska, A., Adamski, C., Radaczyńska, M., Mikiciuk, G., & Mikiciuk, M. (2023) · Biology · doi:10.3390/biology12101298 · Cited by 193
Medical digital twins: enabling precision medicine and medical artificial intelligence
Sadée, C., Testa, S., Barba, T., Hartmann, K., Schuessler, M., Thieme, A. H., Church, G. M., Okoye, I. J., Hernandez‐Boussard, T., Hood, L., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.02.004 · Cited by 167
Self-citationNakach, F.-Z., Idri, A., & Göçeri, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10984-z · Cited by 146
Haghayegh, F., Norouziazad, A., Haghani, E., Feygin, A. A., Rahimi, R. H., Ghavamabadi, H. A., Sadighbayan, D., Madhoun, F., Papagelis, M., Felfeli, T., et al. (2024) · Advanced Science · doi:10.1002/advs.202400595 · Cited by 127
Multimodal Deep Learning Approaches for Precision Oncology: A Comprehensive Review
Yang, H., Yang, M., Chen, J., Yao, G., Zou, Q., & Jia, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae699 · Cited by 115
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 162
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102
“The combination of different data types, processed concurrently, enables these models to provide a holistic view, making them especially effective in applications that require a deep and multifaceted understanding of complex scenarios [113], [161], [162], [160].”
BackgroundPahud de Mortanges, A., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Pöllinger, A., & Reyes, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01190-w · Cited by 93
“Thiscanboosttheirrobustnessand accuracy,enablethediscoveryofnewbiomarkersandtherapeutictargets 6,14 , as well as improve model performance 15 – 17 .”
Big data and artificial intelligence in cancer research
Wu, X., Li, W., & Tu, H. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.10.006 · Cited by 83
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76
Zhan, C., Dai, Z., Yin, S., Carroll, K. C., & Soltanian, M. R. (2024) · Water Research · doi:10.1016/j.watres.2024.121679 · Cited by 77
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
The clinical application of artificial intelligence in cancer precision treatment
Wang, J., Zeng, Z., Li, Z., Liu, G., Zhang, S., Luo, C., Hu, S., Wan, S., & Zhao, L.-Y. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06139-5 · Cited by 84
Microfluidic Nanoparticle Separation for Precision Medicine
Lan, Z., Chen, R., Zou, D., & Zhao, C. (2024) · Advanced Science · doi:10.1002/advs.202411278 · Cited by 68
Bischof, J., Fletcher, G., Verkade, P., Kuntner, C., Fernández-Rodríguez, J., Chaabane, L., Rose, L. A., Walter, A., Vandenbosch, M., van Zandvoort, M. A. M. J., et al. (2024) · npj Imaging · doi:10.1038/s44303-024-00010-w · Cited by 72
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
Mariam, Z., Niazi, S. K., & Magoola, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020079 · Cited by 61
Current Bioinformatics Tools in Precision Oncology
Wolde, T., Bhardwaj, V., & Pandey, V. (2025) · MedComm · doi:10.1002/mco2.70243 · Cited by 67
Chen, Z., Lin, Z., Yang, J., Chen, C., Liu, D., Shan, L., Hu, Y., Guo, T., & Chen, H. (2024) · Nature Communications · doi:10.1038/s41467-024-46246-3 · Cited by 59
Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review
Kumar, S., Rani, S., Sharma, S., & Min, H. (2024) · Bioengineering · doi:10.3390/bioengineering11121233 · Cited by 64
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Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J., Kim, Y., Ghanem, M., Shu, C.-H., James, T., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00974-9 · Cited by 53
Harnessing multimodal data fusion to advance accurate identification of fish feeding intensity
Du, Z., Cui, M., Xu, X., Bai, Z., Han, J., Li, W., Yang, J., Liu, X., Wang, C., & Li, D. (2024) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2024.08.001 · Cited by 52
Deep-learning-enabled multimodal data fusion for lung disease classification
Kumar, S., Ivanova, O., Melyokhin, A., & Tiwari, P. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101367 · Cited by 49
Liu, F., Beck, S., Yang, L., Luo, H., & Zhang, K. C. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00922-2 · Cited by 57
A Comprehensive Review of Multimodal Deep Learning for Enhanced Medical Diagnostics
Al‐Zoghby, A. M., Ebada, A. I., Saleh, A. S., Abdelhay, M., & Awad, W. A. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.065571 · Cited by 55
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Muthamilselvan, S., Sundharbaabu, P. R., & Palaniappan, A. (2023) · Technology in Cancer Research & Treatment · doi:10.1177/15330338231185284 · Cited by 40
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Xu, Y., Cao, L., Chen, Y., Zhang, Z., Liu, W., Li, H., Ding, C., Pu, J., Qian, K., & Xu, W. L. (2024) · Small Methods · doi:10.1002/smtd.202400305 · Cited by 42
Hou, J., Zhang, R., Xie, Y., Li, C., & Qin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02257-y · Cited by 38
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Yassi, M., Chatterjee, A., & Parry, M. F. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad411 · Cited by 40
Zhou, X., Chen, C., Zuo, E., Chen, C., & Lv, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112204 · Cited by 38
Xiong, H., Zhang, X., Sun, J., Xue, Y., Yu, W., Mou, S., Hsia, K. J., Wan, H., & Wang, P. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116820 · Cited by 39
Huang, Y., Chen, X., Cui, Y., Yang, F., Huang, S. X., Li, Z., Ying, Y. J., Li, S. Y., Li, M. H., Gao, P. H., et al. (2025) · Annals of Oncology · doi:10.1016/j.annonc.2025.05.537 · Cited by 40
Wang, Z., Lin, R., Li, Y., Zeng, J., Chen, Y., Ouyang, W., Li, H., Jia, X., Lai, Z., Yu, Y., et al. (2024) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbae012 · Cited by 44
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Yu, Y., Ren, W., Mao, L., Ouyang, W., Hu, Q., Yao, Q., Tan, Y., He, Z., Ban, X., Hu, H., et al. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107765 · Cited by 34
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Jeon, S., Kim, S. H., Heo, G., Heo, H. J., Chae, S. Y., Kwon, Y. W., Lee, S. K., Han, D., Kim, H., Kim, Y. H., et al. (2025) · Advanced Science · doi:10.1002/advs.202509658 · Cited by 29
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Guo, Y., Huo, P., Huang, S., Gou, G., & Li, Q. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202502901 · Cited by 24
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Wang, B., Liu, Q., Zhao, W., Zhang, T., Zhang, D., Sutcharitchan, C., & Li, S. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.12.030 · Cited by 25
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Yang, R., Zhang, L., Bu, F., Fuqiang, S., & Cheng, B. (2024) · BMC Chemistry · doi:10.1186/s13065-024-01219-x · Cited by 21
Han, J., Zhang, H., & Ning, K. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf015 · Cited by 20
Usuzaki, T., Takahashi, K., Inamori, R., Morishita, Y., Takagi, H., Shizukuishi, T., Toyama, Y., Abe, M., Ishikuro, M., Obara, T., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106001 · Cited by 18
Viswan, V., Shaffi, N., Malathy, E., Selvi, G. C., Kavitha, B. R., Abdesselam, A., Wang, S., Suganthan, P. N., Shezawi, I. A., & Mahmud, M. (2026) · Brain Informatics · doi:10.1186/s40708-025-00291-w · Cited by 17
Wang, Y., Zhang, R., Yang, Q., Zhou, Q., Zhang, S., Fan, Y., Huang, L., Li, K., & Zhou, F. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103682 · Cited by 19
Xiao, X., Han, X., Sun, Y., Zheng, G., Miao, Q., Zhang, Y., Tan, J., Liu, G., He, Q., Zhou, J., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00636-4 · Cited by 17
Wang, X., Huang, L., Wang, Y., Guan, R., You, Z., Sheng, N., Xie, X.-P., & Yang, Q.-X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae575 · Cited by 17
Cancer neuroscience in head and neck: interactions, modulation, and therapeutic strategies
Wang, H., Song, X., Shen, H., Liu, W., Wang, Y., Zhang, M., Yang, T., Mou, Y., Ren, C., & Song, X. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02299-6 · Cited by 17
Olawade, D. B., Kade, A., Egbon, E., Usman, S. O., Fapohunda, O., Ijiwade, J. O., & Ogbonna, C. E. (2025) · Molecular Genetics and Genomics · doi:10.1007/s00438-025-02314-x · Cited by 21
Usuzaki, T., Inamori, R., Ishikuro, M., Obara, T., Takaya, E., Homma, N., & Takase, K. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01180-0 · Cited by 16
Unlocking human immune system complexity through AI
Berson, E., Chung, P., Espinosa, C. A., Montine, T. J., & Aghaeepour, N. (2024) · Nature Methods · doi:10.1038/s41592-024-02351-1 · Cited by 18
An Improved Deep Learning Framework for Multimodal Medical Data Analysis
Kumar, S., & Sharma, S. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8100125 · Cited by 16
Recent advances in data-driven fusion of multi-modal imaging and genomics for precision medicine
Wang, S., Liu, M., Li, Y., Zhang, X., Sun, M., Wang, Z., Li, R., Li, Q., Li, Q., He, Y., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102738 · Cited by 19
Wang, X., Yan, F., Li, B., Yu, B., Zhou, X., Tang, X., Jia, T., & Lv, C. (2025) · Plants · doi:10.3390/plants14050786 · Cited by 16
Yeghaian, M., Bodalal, Z., van den Broek, D., Haanen, J. B. A. G., Beets‐Tan, R. G. H., Trebeschi, S., & van Gerven, M. A. J. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf074 · Cited by 16
Wang, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110408 · Cited by 15
Clinicians must participate in the development of multimodal AI
Banerji, C. R. S., Shah, A. B., Dabson, B., Chakraborti, T., Hellon, V., Harbron, C. G., & MacArthur, B. D. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103252 · Cited by 19
Integrating multimodal cancer data using deep latent variable path modelling
Ing, A., Andrades, Á., Cosenza, M. R., & Korbel, J. O. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01052-4 · Cited by 18
Zhou, J., Tang, J., Zuo, Y., Wan, P., Zhang, D., & Shao, W. (2025) · 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/cvpr52734.2025.00971 · Cited by 21
Uyar, B., Savchyn, T., Nilchi, A. N., Sarıgün, A., Wurmus, R., Shaik, M. M., Grüning, B. A., Franke, V., & Akalin, A. (2025) · Nature Communications · doi:10.1038/s41467-025-63688-5 · Cited by 20
Discovery of predictive biomarkers for cancer therapy through computational approaches
Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 24
Chen, X., Xu, H., Yu, S., Hu, W., Zhang, Z., Wang, X., Yuan, Y., Wang, M., Chen, L., Lin, X., et al. (2025) · Biology · doi:10.3390/biology14060651 · Cited by 21
Er, A. G., Ding, D. Y., Er, B., Uzun, M., Cakmak, M., Sadée, C., Durhan, G., Ozmen, M. N., Tanrıöver, M. D., Topeli̇, A., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01128-2 · Cited by 15
Self-citationEr, A. G., Aslan, A. T., Mikulska, M., & Akova, M. (2024) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2024.09.015 · Cited by 14
Zhang, S., Du, S., Sun, C., Li, B., Shao, L., Zhang, L., Wang, K., Liu, Z., & Tian, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72086-4_43 · Cited by 16
Lin, P.-J., Li, W., Zhai, X., Li, Z., Sun, J., Xu, Q., Pan, Y., Ji, L., & Li, C. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3384498 · Cited by 13
Yang, L., Qi, K., Zhang, P., Cheng, J., Soha, H., Jin, Y., Ci, H., Zheng, X., Wang, B., Mei, Y., et al. (2024) · Bioengineering · doi:10.3390/bioengineering11050429 · Cited by 13
Robust multi-modal fusion architecture for medical data with knowledge distillation
Wang, M., Fan, S., Li, Y., Gao, B., Xie, Z., & Chen, H. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108568 · Cited by 14
Lee, J., Jang, J. H., Eum, H., Jang, H. W., Kim, M., Park, S., Park, C. K., Choi, S. H., Ahn, S. S., Han, Y., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02018-x · Cited by 15
Applications of Metabolomics and Lipidomics in the Neonatal Intensive Care Unit
Reiss, J. D., Mataraso, S. J., Holzapfel, L. F., Marić, I., Kasowski, M. M., Martin, C. R., Long, J. Z., Stevenson, D. K., & Shaw, G. M. (2025) · NeoReviews · doi:10.1542/neo.26-2-011 · Cited by 14
Zhang, Y., Ji, L., Wen, H., Chu, Y., Xing, W., Tian, G., Yao, Y., & Yang, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107586 · Cited by 12
mBGT: Encoding Brain Signals With Multimodal Brain Graph Transformer
Peng, C., Guo, T., Xie, C., Bai, X., Zhou, J., Zhao, X., He, E., & Xia, F. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3425478 · Cited by 11
Metadata
- Authors
- Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Steyaert, Sandra
- Gevaert, Olivier
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2023-04-06
- Volume
- 5
- Issue
- 4
- Pages
- 351-362
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC10484010
- OpenAlex
- W4362673398
- Semantic Scholar
- 304b640c1b2559ac1442ac9be54853ac80ec248c
- Cited by
- 402
- References
- 159
- Citations by year
- 2026: 122 · 2025: 173 · 2024: 92 · 2023: 15
- FWCI
- 66.57
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Funders
- National Cancer Institute (R01CA260271)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- multimodal fusion, cancer biomarker discovery, deep learning, data sparsity, interpretability, electronic health records, digital pathology, radiographic images, molecular data, data standardization, personalized medicine, cancer diagnosis
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