Survey:  Big data application in biomedical research

Yvonne Bachiller; Peter Busch; Manolya Kavakli; Len Hamey

doi:10.1145/3192975.3192986

Survey: Big data application in biomedical research

Yvonne Bachiller, Peter Busch, Manolya Kavakli, Len Hamey

Research output: Unpublished contribution to conference › Unpublished Conference Paper › peer-review

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 language	English
Pages	174-178
Number of pages	5
DOIs	https://doi.org/10.1145/3192975.3192986
Publication status	Published - 24 Feb 2018
Event	10th International Conference on Computer and Automation Engineering, ICCAE 2018 - Brisbane, Australia Duration: 24 Feb 2018 → 26 Feb 2018

Conference

Conference	10th International Conference on Computer and Automation Engineering, ICCAE 2018
Country/Territory	Australia
City	Brisbane
Period	24/02/18 → 26/02/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

Algorithms
Big data
Biomedicine
Deep learning
Disease diagnosis
Machine learning

Access to Document

10.1145/3192975.3192986

Cite this

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title = "Survey: Big data application in biomedical research",

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.",

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AU - Bachiller, Yvonne

AU - Busch, Peter

AU - Kavakli, Manolya

AU - Hamey, Len

PY - 2018/2/24

Y1 - 2018/2/24

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AB - 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.

KW - Algorithms

KW - Big data

KW - Biomedicine

KW - Deep learning

KW - Disease diagnosis

KW - Machine learning

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DO - 10.1145/3192975.3192986

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Y2 - 24 February 2018 through 26 February 2018

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