From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research

Abstract 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…

• CP
  Cancer Prevention and Research Institute of Texas

  Awards: CPRIT RP240117, P30CA016672, RP240117
• NI
  National Institutes of Health

  Awards: P30CA016672, CPRIT RP240117, R01CA276178, U24CA224285, R01CA262425
• UO
  University of Texas MD Anderson Cancer Center

  Awards: P30CA016672, U24CA224285
• NC
  National Cancer Institute

  Awards: P30CA016672, U24CA224285