The mutual information: Detecting and evaluating dependencies between variables

By Ralph E. Steuer, Jürgen Kurths, Carsten O. Daub, John Weise, Joachim Selbig. European Conference on Computational Biology, 2002

836 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3dcbd15d54632631bf6a8697647e3fa837d31de3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Statistics and Probability

Citations in the corpus

Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors (2009, background): “It is well known that estimating MI from a finite set of sequences will result in a finite-size error [56,59].”
Reverse engineering of regulatory networks in human B cells (2005, methodology): “For the synthetic analysis, this is done using a Gaussian Kernel estimato”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “show that mutual information and the Pearson correlation have an almost oneto-one correspondence when measuring gene pairwise relationships within their investigated data set, justifying the application of Pearson correlation as a measure of similarity for gene-expression measurements [14].”
Universal Estimation of Information Measures for Analog Sources (2009, background): “See [230] for a tutorial on this topic.”
Causality detection based on information-theoretic approaches in time series analysis (2007, methodology): “[221] that the estimated MI is much less sensitive than the probability density itself.”
Equitability, mutual information, and the maximal information coefficient (2013, methodology): “medical imaging [38], and signal processing [39]. We also note that mutual information has been used by multiple groups to construct networks analogous to those constructed by Reshef et al. using MIC [3, 40, 41, 42, 43]. We emphasize, however, that all the diculties associated with estimating mutual”
How to infer gene networks from expression profiles (2007, methodology): “In practical application, however, MI and Pearson correlation may yield almost identical results (Steuer et al , 2002).”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “Currently, several models have been proposed for GRN construction, such as information theory models (Steuer et al. 2002; Stuart et al. 2003); Boolean networks (Kauffman 1969; Thomas 1973; Bornholdt 2008); Bayesian networks (Heckerman 1996; Imoto et al. 2003; Needham et al. 2007; Werhli and…”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Besides correlation coefficients, also Euclidean distances and information theoretic scores, such as the mutual information, were applied to detect gene regulatory dependencies (Steuer et al., 2002).”
Measure of Correlation between River Flows Using the Copula-Entropy Method (2013, methodology): “Some of advantages of this method have been reported widely (e.g., Li 1990; Singh 2000; Steuer et al. 2002).”
Feature selection methods for big data bioinformatics: A survey from the search perspective. (2016, background): “Examples of data-distilled feature importance measures include t-test [106,107], fold-change difference [108], Z-score [109], Pearson correlation coefficient [110], relative entropy [111], mutual information [112,113], separability-correlation measure [16], feature relevance [114,115], label changes”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “Despite its useful properties, a possible reason for why MI has not been more widely adopted, particularly within the neuroimaging community, is the difficulty of accurately estimating MI from limited quantities of experimental data (Steuer et al., 2002).”
Nonparametric independence testing via mutual information (2017, methodology): “Often we would like to assess whether or not the assumption of independence is reasonable, for instance as a method of exploratory data analysis (Steuer et al., 2002; Albert et al., 2015; Nguyen and Eisenstein, 2017), or as a way of evaluating the goodness-of-ﬁt of a statistical model (Einmahl and…”