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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer

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    Foundation models (FMs) are revolutionizing cancer research by integrating diverse data types. This review maps the shift from traditional machine learning to FMs for multimodal data integration in oncology, paving the way for AI-driven discoveries.

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    Area of Science:

    • Computational oncology
    • Artificial intelligence in medicine
    • Multimodal data integration

    Background:

    • Cancer research increasingly relies on integrating diverse data modalities like genomics, proteomics, imaging, and clinical data.
    • Extracting actionable insights from these complex, heterogeneous datasets presents a significant challenge.
    • Foundation models (FMs) offer promising avenues for biomarker discovery, improved diagnostics, and personalized cancer treatment.

    Purpose of the Study:

    • To provide a comprehensive review of multimodal data integration strategies in oncology.
    • To examine emerging trends in machine learning (ML) and deep learning (DL), focusing on the transition to FMs.
    • To identify state-of-the-art FMs, repositories, and tools for advancing data-driven cancer research.

    Main Methods:

    • Systematic review of literature on multimodal data integration in oncology.
    • Analysis of machine learning (ML) and deep learning (DL) methodologies, including foundation models (FMs).
    • Examination of trends, frameworks, validation protocols, and open-source resources.

    Main Results:

    • The study maps the transition from conventional ML to advanced FMs for multimodal data integration in oncology.
    • Identifies key advancements, challenges, and state-of-the-art FMs for integrating multi-omics and imaging data.
    • Highlights publicly available multimodal repositories and tools crucial for data integration.

    Conclusions:

    • Current integrative methods using FMs lay the groundwork for next-generation large-scale AI models in oncology.
    • This review is the first to systematically chart the evolution from ML to FMs in multimodal cancer data integration.
    • These advancements are foundational for the future of AI-driven cancer research and personalized medicine.