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Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map.

Authors :
Miagoux, Quentin
Singh, Vidisha
de Mézquita, Dereck
Chaudru, Valerie
Elati, Mohamed
Petit-Teixeira, Elisabeth
Niarakis, Anna
Source :
Journal of Personalized Medicine. Aug2021, Vol. 11 Issue 8, p785. 1p.
Publication Year :
2021

Abstract

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754426
Volume :
11
Issue :
8
Database :
Academic Search Index
Journal :
Journal of Personalized Medicine
Publication Type :
Academic Journal
Accession number :
152128881
Full Text :
https://doi.org/10.3390/jpm11080785