Survey: Big Data Application in Biomedical Research

被引：1

作者：

Bachiller, Yvonne ^{[1
]}

Busch, Peter ^{[2
]}

Kavakli, Manolya ^{[2
]}

Hamey, Len ^{[2
]}

机构：

[1] Dept Comp, 33 Wilding St, Sydney, NSW, Australia

[2] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia

来源：

PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2018) | 2018年

关键词：

Big data; machine learning; deep learning; algorithms; disease diagnosis; biomedicine;

D O I：

10.1145/3192975.3192986

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：174 / 178

页数：5