Mutual information between discrete and continuous data sets.
Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimat...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3929353?pdf=render |
| _version_ | 1818050035099631616 |
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| author | Brian C Ross |
| author_facet | Brian C Ross |
| author_sort | Brian C Ross |
| collection | DOAJ |
| description | Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen-Shannon divergence of two or more data sets. |
| first_indexed | 2024-12-10T10:47:04Z |
| format | Article |
| id | doaj.art-1ae760d8d1694e508ef4fd8e0fcdf003 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T10:47:04Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1ae760d8d1694e508ef4fd8e0fcdf0032022-12-22T01:52:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8735710.1371/journal.pone.0087357Mutual information between discrete and continuous data sets.Brian C RossMutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen-Shannon divergence of two or more data sets.http://europepmc.org/articles/PMC3929353?pdf=render |
| spellingShingle | Brian C Ross Mutual information between discrete and continuous data sets. PLoS ONE |
| title | Mutual information between discrete and continuous data sets. |
| title_full | Mutual information between discrete and continuous data sets. |
| title_fullStr | Mutual information between discrete and continuous data sets. |
| title_full_unstemmed | Mutual information between discrete and continuous data sets. |
| title_short | Mutual information between discrete and continuous data sets. |
| title_sort | mutual information between discrete and continuous data sets |
| url | http://europepmc.org/articles/PMC3929353?pdf=render |
| work_keys_str_mv | AT briancross mutualinformationbetweendiscreteandcontinuousdatasets |