Survey

big data applications in biomedical research

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

In recent years, the emergence of diverse applications created a plethora of data and immense sources that can be applied in varying areas of the industry worldwide escalating its capabilities including biomedicine. Subsequently, analytical tools loomed to leverage the availability of massive data to analyze and elicit meaningful information to improve biomedical research and enhance healthcare systems. Algorithms applied in these analytical tools supplements the prognosis of diseases and personalized treatment of fatal diseases. This survey will evaluate algorithms used in bio medical research for personalized precision medicine, dissect the characteristics that made breakthrough in improving the efficiency of the analytical tool and identify the possible applicability in other diseases. It will focus on the machine learning and deep learning algorithms, both supervised and unsupervised that is applied in terminal diseases.
Original languageEnglish
Title of host publicationICCAE 2018
Subtitle of host publicationProceedings of 2018 10th International Conference on Computer and Automation Engineering
PublisherAssociation for Computing Machinery
Pages174-178
Number of pages5
Volume2018-April
ISBN (Electronic)9781450364102
DOIs
Publication statusPublished - 2018
EventICCAE 2018: 10th International Conference on Computer and Automation Engineering - Brisbane, Australia
Duration: 24 Feb 201826 Feb 2018

Conference

ConferenceICCAE 2018
CountryAustralia
CityBrisbane
Period24/02/1826/02/18

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Keywords

  • big data
  • machine learning
  • deep learning
  • algorithms
  • disease diagnosis
  • biomedicine

Cite this

Bachiller, Y., Busch, P., Kavakli, M., & Hamey, L. (2018). Survey: big data applications in biomedical research. In ICCAE 2018: Proceedings of 2018 10th International Conference on Computer and Automation Engineering (Vol. 2018-April, pp. 174-178). Association for Computing Machinery. https://doi.org/10.1145/3192975.3192986