ArticleJournal of computational biology : a journal of computational molecular cell biology2015
Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Article
- Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty.Entropy (Basel, Switzerland) · 2021Article
- Toward an Information Theory of Quantitative Genetics.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- New amino acid substitution matrix brings sequence alignments into agreement with structure matches.Proteins · 2021Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- Cerebrospinal Fluid MicroRNA Changes in Cognitively Normal Veterans With a History of Deployment-Associated Mild Traumatic Brain Injury.Frontiers in neuroscience · 2021Article
- Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information.Entropy (Basel, Switzerland) · 2020Article
- Systems Genetics for Mechanistic Discovery in Heart Diseases.Circulation research · 2020Review
- Article
- Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies.G3 (Bethesda, Md.) · 2019Article
- Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability Function.Journal of computational biology : a journal of computational molecular cell biology · 2019Article
- Symmetries among Multivariate Information Measures Explored Using Möbius Operators.Entropy (Basel, Switzerland) · 2019Article
- Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application.BMC genomics · 2018Article
- The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
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2 authors.
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Abstract
Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables. The proposed dependence measure for a subset of variables, τ, differential interaction information, Δ(τ), has the property that for subsets of τ some of the factors of Δ(τ) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds.
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