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Journal of Medical Systems

, 42:139 | Cite as

A Survey of Data Mining and Deep Learning in Bioinformatics

  • Kun Lan
  • Dan-tong Wang
  • Simon FongEmail author
  • Lian-sheng Liu
  • Kelvin K. L. Wong
  • Nilanjan Dey
Image & Signal Processing
First Online:
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

Keywords

Bioinformatics Biomedicine Data mining Machine learning Deep learning 
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Notes

Funding

The authors are thankful to the financial support from the research grants, 1) MYRG2015–00024-FST, titled Building Sustainable Knowledge Networks through Online Communities’ offered by RDAO/FST, University of Macau and Macau SAR government. 2) MYRG2016–00069, titled ‘Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance’ offered by RDAO/FST, University of Macau and Macau SAR government. 3) FDCT/126/2014/A3, titled ‘A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel’ offered by FDCT of Macau SAR government.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that this article content has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Authors and Affiliations

  • Kun Lan
    • 1
  • Dan-tong Wang
    • 2
  • Simon Fong
    • 1
    Email author
  • Lian-sheng Liu
    • 3
  • Kelvin K. L. Wong
    • 4
  • Nilanjan Dey
    • 5
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauChina
  2. 2.Department of Media integration technology centerZhejiang Radio & TV GroupHangzhouPeople’s Republic of China
  3. 3.First Affiliated Hospital of Guangzhou University of TCMGuangzhouChina
  4. 4.School of MedicineUniversity of Western SydneySydneyAustralia
  5. 5.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

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