title: Big data bioinformatics.
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.
authors.affiliation: 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.
authors.forename: Casey S
authors.initials: CS
authors.lastname: Greene
authors.forename: Jie
authors.initials: J
authors.lastname: Tan
authors.forename: Matthew
authors.initials: M
authors.lastname: Ung
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling; D057166:High-Throughput Screening Assays; D006801:Humans; D012984:Software