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Massive datasets and machine learning for computational biomedicine: trends and challenges

Author

Listed:
  • Anton Kocheturov

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida
    National Research University Higher School of Economics)

  • Athanasia Karakitsiou

    (Technological Educational Institute of Central Macedonia)

Abstract

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine.

Suggested Citation

  • Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-018-2891-2
    DOI: 10.1007/s10479-018-2891-2
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    2. Renu Sabharwal & Shah J. Miah & Samuel Fosso Wamba, 2025. "Extending artificial intelligence research in the clinical domain: a theoretical perspective," Annals of Operations Research, Springer, vol. 348(3), pages 1713-1744, May.
    3. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    4. Kanchan Jha & Sriparna Saha & Pratik Dutta, 2024. "Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach," Annals of Operations Research, Springer, vol. 339(3), pages 1793-1811, August.
    5. Lasse Bohlen & Julian Rosenberger & Patrick Zschech & Mathias Kraus, 2025. "Leveraging interpretable machine learning in intensive care," Annals of Operations Research, Springer, vol. 347(2), pages 1093-1132, April.
    6. Dujuan Wang & Xinwei Wang & Mohammad Zoynul Abedin & Sutong Wang & Yunqiang Yin, 2025. "Interpretable multi-hop knowledge reasoning for gastrointestinal disease," Annals of Operations Research, Springer, vol. 347(2), pages 959-990, April.
    7. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    8. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
    9. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    10. Viswanath Venkatesh, 2022. "Adoption and use of AI tools: a research agenda grounded in UTAUT," Annals of Operations Research, Springer, vol. 308(1), pages 641-652, January.
    11. Xiaotong Sun & Wei Xu & Hongxun Jiang & Qili Wang, 2021. "A deep multitask learning approach for air quality prediction," Annals of Operations Research, Springer, vol. 303(1), pages 51-79, August.

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