The mutual information: Detecting and evaluating dependencies between
Journal Article
,
Search for other works by this author on:
,
Search for other works by this author on:
,
Search for other works by this author on:
,
Search for other works by this author on:
Search for other works by this author on:
Published:
01 October 2002
Cite
R. Steuer, J. Kurths, C. O. Daub, J. Weise, J. Selbig, The mutual information: Detecting and evaluating dependencies between variables, Bioinformatics, Volume 18, Issue suppl_2, October 2002, Pages S231–S240, https://doi.org/10.1093/bioinformatics/18.suppl_2.S231
Close
Navbar Search Filter Mobile Enter search term Search
Abstract
Motivation: Clustering co-expressed genes usually requires the definition of `distance' or `similarity' between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data.
Results: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.
Contact: steuer@agnld.uni-potsdam.de
© Oxford University Press 2002
Citations
Views
Altmetric
Metrics
Total Views 4,462
1,582 Pageviews
2,880 PDF Downloads
Since 11/1/2016
Month: | Total Views: |
---|---|
November 2016 | 9 |
December 2016 | 7 |
January 2017 | 26 |
February 2017 | 60 |
March 2017 | 62 |
April 2017 | 24 |
May 2017 | 42 |
June 2017 | 57 |
July 2017 | 55 |
August 2017 | 38 |
September 2017 | 62 |
October 2017 | 50 |
November 2017 | 52 |
December 2017 | 74 |
January 2018 | 96 |
February 2018 | 80 |
March 2018 | 83 |
April 2018 | 59 |
May 2018 | 22 |
June 2018 | 38 |
July 2018 | 38 |
August 2018 | 43 |
September 2018 | 26 |
October 2018 | 29 |
November 2018 | 31 |
December 2018 | 33 |
January 2019 | 38 |
February 2019 | 45 |
March 2019 | 48 |
April 2019 | 35 |
May 2019 | 41 |
June 2019 | 52 |
July 2019 | 48 |
August 2019 | 41 |
September 2019 | 54 |
October 2019 | 40 |
November 2019 | 51 |
December 2019 | 34 |
January 2020 | 44 |
February 2020 | 37 |
March 2020 | 27 |
April 2020 | 44 |
May 2020 | 44 |
June 2020 | 47 |
July 2020 | 47 |
August 2020 | 21 |
September 2020 | 45 |
October 2020 | 46 |
November 2020 | 58 |
December 2020 | 50 |
January 2021 | 42 |
February 2021 | 51 |
March 2021 | 62 |
April 2021 | 46 |
May 2021 | 57 |
June 2021 | 43 |
July 2021 | 44 |
August 2021 | 43 |
September 2021 | 37 |
October 2021 | 44 |
November 2021 | 63 |
December 2021 | 52 |
January 2022 | 49 |
February 2022 | 34 |
March 2022 | 51 |
April 2022 | 55 |
May 2022 | 61 |
June 2022 | 52 |
July 2022 | 42 |
August 2022 | 47 |
September 2022 | 43 |
October 2022 | 39 |
November 2022 | 45 |
December 2022 | 30 |
January 2023 | 50 |
February 2023 | 74 |
March 2023 | 106 |
April 2023 | 96 |
May 2023 | 80 |
June 2023 | 95 |
July 2023 | 50 |
August 2023 | 29 |
September 2023 | 45 |
October 2023 | 40 |
November 2023 | 31 |
December 2023 | 38 |
January 2024 | 38 |
February 2024 | 37 |
March 2024 | 32 |
April 2024 | 46 |
May 2024 | 45 |
June 2024 | 24 |
July 2024 | 39 |
August 2024 | 34 |
September 2024 | 40 |
October 2024 | 28 |
Citations
528 Web of Science
×
Email alerts
Citing articles via
More from Oxford Academic