Finding Correlations in Big Data. Biotechnology, N. 30(4):334–335.
Finding Correlations in Big Data [link]Paper  doi  abstract   bibtex   
In today's era of large data sets, statistical methods that facilitate exploratory analyses to detect patterns and generate hypotheses are critical to progress in biology. Last year, David Reshef and colleagues published a new approach to such analysis, called maximal information criteria or MIC (Science 334, 1518-1524, 2011). Nature Biotechnology solicited comments from several practitioners versed in data-intensive biological research. Their responses not only highlight the appeal of methods like MIC for biological research, but also raise some important reservations as to its widespread use and statistical power.
@article{biotechnologyFindingCorrelationsBig2012,
  title = {Finding Correlations in Big Data},
  author = {Biotechnology, Nature},
  date = {2012-04},
  journaltitle = {Nature Biotechnology},
  volume = {30},
  pages = {334--335},
  issn = {1087-0156},
  doi = {10.1038/nbt.2182},
  url = {https://doi.org/10.1038/nbt.2182},
  abstract = {In today's era of large data sets, statistical methods that facilitate exploratory analyses to detect patterns and generate hypotheses are critical to progress in biology. Last year, David Reshef and colleagues published a new approach to such analysis, called maximal information criteria or MIC (Science 334, 1518-1524, 2011). Nature Biotechnology solicited comments from several practitioners versed in data-intensive biological research. Their responses not only highlight the appeal of methods like MIC for biological research, but also raise some important reservations as to its widespread use and statistical power.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-10553101,big-data,correlation-analysis,distance-correlation,editorial,interview,mic,nonlinear-correlation,statistics},
  number = {4}
}
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