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  "Title": "Information Theoretic Analysis of Gene Expression Data",
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  "Description": "Implements Surprisal Analysis for gene expression data\nsuch as RNA-seq or microarray experiments. Surprisal Analysis\nis an information-theoretic method that decomposes gene\nexpression data into a baseline state and constraint-associated\ndeviations, which helps to capture coordinated gene expression\npatterns under different biological conditions. References\nwhere Surprisal analysis has been used for analyzing gene\nexpression data (using the same methodology provided within\nthis R package) are Kravchenko-Balasha et al (2014)\n<doi:10.1371/journal.pone.0108549>, Zadran et al. (2014)\n<doi:10.1073/pnas.1414714111>, Su et al. (2019)\n<doi:10.1371/journal.pcbi.1007034>, Bogaert et al. (2018)\n<doi:10.1371/journal.pone.0195142>.",
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