Survey: big data applications in biomedical research

Yvonne Bachiller; Peter Busch; Manolya Kavakli; Len Hamey

doi:10.1145/3192975.3192986

Survey: big data applications in biomedical research

Yvonne Bachiller, Peter Busch, Manolya Kavakli, Len Hamey

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

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
Title of host publication	ICCAE 2018
Subtitle of host publication	Proceedings of 2018 10th International Conference on Computer and Automation Engineering
Publisher	Association for Computing Machinery
Pages	174-178
Number of pages	5
Volume	2018-April
ISBN (Electronic)	9781450364102
DOIs	https://doi.org/10.1145/3192975.3192986
Publication status	Published - 2018
Event	ICCAE 2018: 10th International Conference on Computer and Automation Engineering - Brisbane, Australia Duration: 24 Feb 2018 → 26 Feb 2018

Conference

Conference	ICCAE 2018
Country/Territory	Australia
City	Brisbane
Period	24/02/18 → 26/02/18

Keywords

big data
machine learning
deep learning
algorithms
disease diagnosis
biomedicine

Access to Document

10.1145/3192975.3192986

Cite this

@inproceedings{fdabd765db914dbba5ea971c02aa07b0,

title = "Survey: big data applications 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.",

keywords = "big data, machine learning, deep learning, algorithms, disease diagnosis, biomedicine",

author = "Yvonne Bachiller and Peter Busch and Manolya Kavakli and Len Hamey",

year = "2018",

doi = "10.1145/3192975.3192986",

language = "English",

volume = "2018-April",

pages = "174--178",

booktitle = "ICCAE 2018",

publisher = "Association for Computing Machinery",

note = "ICCAE 2018 : 10th International Conference on Computer and Automation Engineering ; Conference date: 24-02-2018 Through 26-02-2018",

}

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, Association for Computing Machinery, pp. 174-178, ICCAE 2018, Brisbane, Queensland, Australia, 24/02/18. https://doi.org/10.1145/3192975.3192986

Survey : big data applications in biomedical research. / Bachiller, Yvonne ; Busch, Peter ; Kavakli, Manolya ; Hamey, Len.

ICCAE 2018: Proceedings of 2018 10th International Conference on Computer and Automation Engineering. Vol. 2018-April Association for Computing Machinery, 2018. p. 174-178.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Survey

T2 - ICCAE 2018

AU - Bachiller, Yvonne

AU - Busch, Peter

AU - Kavakli, Manolya

AU - Hamey, Len

PY - 2018

Y1 - 2018

N2 - 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.

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 - big data

KW - machine learning

KW - deep learning

KW - algorithms

KW - disease diagnosis

KW - biomedicine

UR - http://www.scopus.com/inward/record.url?scp=85052020176&partnerID=8YFLogxK

U2 - 10.1145/3192975.3192986

DO - 10.1145/3192975.3192986

M3 - Conference proceeding contribution

AN - SCOPUS:85052020176

VL - 2018-April

SP - 174

EP - 178

BT - ICCAE 2018

PB - Association for Computing Machinery

Y2 - 24 February 2018 through 26 February 2018

ER -

Survey: big data applications in biomedical research

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this