ohno

<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 (‘ohnolog’) or from subsequent small scale duplications (‘SSD’)
as well as copy number variants (‘CNV’).</p>

datasets

data

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:
Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.

Vincent Cabeli

miic

Learning Causal or Non-Causal Graphical Models Using Information
Theory

Honghao Li

Marcel Ribeiro Dantas

Nadir Sella

Louis Verny

Severine Affeldt

Hervé Isambert

ohno function

Format

Tetraploidization in vertebrate evolution — ohno

Tetraploidization in vertebrate evolution

ohno: Tetraploidization in vertebrate evolution

Description

Usage

Arguments

Format

References