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Cellular and Molecular Biology

High-dimensional role of AI and machine learning in cancer research

British Journal of Cancer volume 126, pages 523–532 (2022)Cite this article

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Abstract

The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as ‘Intelligent Systems’ (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients’ outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).

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Fig. 1: Impacts Overview.
Fig. 2: Methodological Approach.
Fig. 3: Integrated View.

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Acknowledgements

The author acknowledges NSF support from grant NSF 19-500. DMS 1918925/1922843 (years: 08/01/2019 – 08/01/2022).

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  1. Institute of Data Science & Computing, University of Miami, 1320 S. Dixie Highway, 600, Coral Gables, FL, 33146, USA

    Enrico Capobianco

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Capobianco, E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer 126, 523–532 (2022). https://doi.org/10.1038/s41416-021-01689-z

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