ohno_stateOrder

<p>20,415 protein-coding genes in the human genome from Ensembl (v70) and information on the
retention of duplicates originating either from the two whole genome duplications at
the onset of vertebrates (&lt;U+2018&gt;ohnolog&lt;U+2019&gt;) or from subsequent small scale duplications (&lt;U+2018&gt;SSD&lt;U+2019&gt;)
as well as copy number variants (&lt;U+2018&gt;CNV&lt;U+2019&gt;), cathegory order.</p>

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We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.

Nadir Sella

miic

Multivariate Information Inductive Causation

Louis Verny

Severine Affeldt

Herv<c3><a9> Isambert

ohno_stateOrder function

Format

Tetraploidization in vertebrate evolution — ohno_stateOrder

Tetraploidization in vertebrate evolution

ohno_stateOrder: Tetraploidization in vertebrate evolution

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References