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Big Data Bioinformatics
Author(s) -
Greene Casey S.,
Tan Jie,
Ung Matthew,
Moore Jason H.,
Cheng Chao
Publication year - 2014
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.24662
Subject(s) - big data , computer science , profiling (computer programming) , data science , machine learning , key (lock) , artificial intelligence , data mining , programming language , computer security
Recent technological advances allow for high throughput profiling of biological systems in a cost‐efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. J. Cell. Physiol. 229: 1896–1900, 2014. © 2014 Wiley Periodicals, Inc.