From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911619154444288 |
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| author | Muneer, Amgad Waqas, Muhammad Saad, Maliazurina B Showkatian, Eman Bandyopadhyay, Rukhmini Xu, Hui Li, Wentao Chang, Joe Y Liao, Zhongxing Haymaker, Cara Soto, Luisa Solis Wu, Carol C Vokes, Natalie I Le, Xiuning Byers, Lauren A Gibbons, Don L Heymach, John V Zhang, Jianjun Wu, Jia |
| author_facet | Muneer, Amgad Waqas, Muhammad Saad, Maliazurina B Showkatian, Eman Bandyopadhyay, Rukhmini Xu, Hui Li, Wentao Chang, Joe Y Liao, Zhongxing Haymaker, Cara Soto, Luisa Solis Wu, Carol C Vokes, Natalie I Le, Xiuning Byers, Lauren A Gibbons, Don L Heymach, John V Zhang, Jianjun Wu, Jia |
| contents | Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and heterogeneous datasets remains a key challenge. The rise of foundation models (FMs) -- large deep-learning models pretrained on extensive amounts of data serving as a backbone for a wide range of downstream tasks -- offers new avenues for discovering biomarkers, improving diagnosis, and personalizing treatment. This paper presents a comprehensive review of widely adopted integration strategies of multimodal data to assist advance the computational approaches for data-driven discoveries in oncology. We examine emerging trends in machine learning (ML) and deep learning (DL), including methodological frameworks, validation protocols, and open-source resources targeting cancer subtype classification, biomarker discovery, treatment guidance, and outcome prediction. This study also comprehensively covers the shift from traditional ML to FMs for multimodal integration. We present a holistic view of recent FMs advancements and challenges faced during the integration of multi-omics with advanced imaging data. We identify the state-of-the-art FMs, publicly available multi-modal repositories, and advanced tools and methods for data integration. We argue that current state-of-the-art integrative methods provide the essential groundwork for developing the next generation of large-scale, pre-trained models poised to further revolutionize oncology. To the best of our knowledge, this is the first review to systematically map the transition from conventional ML to advanced FM for multimodal data integration in oncology, while also framing these developments as foundational for the forthcoming era of large-scale AI models in cancer research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research Muneer, Amgad Waqas, Muhammad Saad, Maliazurina B Showkatian, Eman Bandyopadhyay, Rukhmini Xu, Hui Li, Wentao Chang, Joe Y Liao, Zhongxing Haymaker, Cara Soto, Luisa Solis Wu, Carol C Vokes, Natalie I Le, Xiuning Byers, Lauren A Gibbons, Don L Heymach, John V Zhang, Jianjun Wu, Jia Quantitative Methods Artificial Intelligence Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and heterogeneous datasets remains a key challenge. The rise of foundation models (FMs) -- large deep-learning models pretrained on extensive amounts of data serving as a backbone for a wide range of downstream tasks -- offers new avenues for discovering biomarkers, improving diagnosis, and personalizing treatment. This paper presents a comprehensive review of widely adopted integration strategies of multimodal data to assist advance the computational approaches for data-driven discoveries in oncology. We examine emerging trends in machine learning (ML) and deep learning (DL), including methodological frameworks, validation protocols, and open-source resources targeting cancer subtype classification, biomarker discovery, treatment guidance, and outcome prediction. This study also comprehensively covers the shift from traditional ML to FMs for multimodal integration. We present a holistic view of recent FMs advancements and challenges faced during the integration of multi-omics with advanced imaging data. We identify the state-of-the-art FMs, publicly available multi-modal repositories, and advanced tools and methods for data integration. We argue that current state-of-the-art integrative methods provide the essential groundwork for developing the next generation of large-scale, pre-trained models poised to further revolutionize oncology. To the best of our knowledge, this is the first review to systematically map the transition from conventional ML to advanced FM for multimodal data integration in oncology, while also framing these developments as foundational for the forthcoming era of large-scale AI models in cancer research. |
| title | From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2507.09028 |