Format

Send to

Choose Destination
J Cell Physiol. 2014 Dec;229(12):1896-900. doi: 10.1002/jcp.24662.

Big data bioinformatics.

Author information

1
Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.

Abstract

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.

PMID:
24799088
PMCID:
PMC5604462
DOI:
10.1002/jcp.24662
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center