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Annals of Operations Research

, Volume 276, Issue 1–2, pp 5–34 | Cite as

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

  • Anton KocheturovEmail author
  • Panos M. Pardalos
  • Athanasia Karakitsiou
S.I.: Computational Biomedicine

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.

Notes

Acknowledgements

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

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Applied OptimizationUniversity of FloridaGainesvilleUSA
  2. 2.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia
  3. 3.Department of Business AdministrationTechnological Educational Institute of Central MacedoniaSerresGreece

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