Premium
Bayesian networks for cell differentiation process assessment
Author(s) -
Di Serio Clelia,
Scala Serena,
Vicard Paola
Publication year - 2020
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.287
Subject(s) - bayesian network , probabilistic logic , bayesian probability , computer science , peripheral blood , confounding , cellular differentiation , computational biology , process (computing) , gene regulatory network , statistical model , machine learning , artificial intelligence , gene , biology , medicine , immunology , gene expression , genetics , operating system
The way cell differentiate from bone marrow to peripheral blood level plays a crucial role in understanding and treating rare diseases and more common tumours. The main goal of this paper is to introduce a flexible statistical framework able to describe the cell differentiation process and to reconstruct a dependence structure along different levels of differentiation. We use next generation sequencing data on haematological diseases (severe combined immunodeficiency) within a gene therapy framework. The proposed statistical approach is based on Bayesian networks (BNs) and aims at finding a probabilistic model to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions among genes or the confounding effects of experimental conditions. Bayesian networks enable analyses on gene therapy‐treated patients in a data‐driven fashion and allow for exploring all relationships among different blood cell types integrating biological information, subject‐matter knowledge, and probabilistic principles.