Scalable biclustering — the future of big data exploration?-Reference-Cited by-同舟云学术

Scalable biclustering — the future of big data exploration?

Published:2019-06-28 Issue:7 Volume:8 Page:
ISSN:2047-217X
Container-title:GigaScience
language:en
Short-container-title:

Author:

Orzechowski Patryk¹²^ORCID,Boryczko Krzysztof³^ORCID,Moore Jason H¹^ORCID

Affiliation:

1. Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA

2. Department of Automatics and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, Kraków 30-059, Poland

3. Department of Computer Science, AGH University of Science and Technology, al. A. Mickiewicza 30, Kraków 30-059, Poland

Abstract

Abstract Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Link

http://academic.oup.com/gigascience/article-pdf/8/7/giz078/28880874/giz078.pdf

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