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

  • S.I.: Computational Biomedicine
  • Published:
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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.

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Notes

  1. In the appendix, see Table 3 for the full list of research areas available in the Web of Knowledge that we related to biomedical research

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Acknowledgements

Panos Pardalos was partially supported by Laboratory of Algorithm and Technologies for Network Analysis, Nizhny Novgorod, Russia.

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Authors and Affiliations

  1. Center for Applied Optimization, University of Florida, 401 Weil Hall, P.O. Box 116595, Gainesville, FL, 32611-6595, USA

    Anton Kocheturov & Panos M. Pardalos

  2. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova, Nizhny Novgorod, Russia, 603093

    Panos M. Pardalos

  3. Department of Business Administration, Technological Educational Institute of Central Macedonia, Terma Magnisias, 61100, Serres, Greece

    Athanasia Karakitsiou

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  1. Anton Kocheturov
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Correspondence to Anton Kocheturov.

Appendix

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See Tables 2 and 3.

Table 2 Key words used for the searches
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Table 3 Research areas defined in Web of Science that we marked as related to Biomedicine
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Kocheturov, A., Pardalos, P.M. & Karakitsiou, A. Massive datasets and machine learning for computational biomedicine: trends and challenges. Ann Oper Res 276, 5–34 (2019). https://doi.org/10.1007/s10479-018-2891-2

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