Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.

Autor:	Hemedan AA; Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg., Satagopam V; Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg., Schneider R; Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg., Ostaszewski M; Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Jazyk:	angličtina
Zdroj:	IScience [iScience] 2024 Sep 14; Vol. 27 (10), pp. 110956. Date of Electronic Publication: 2024 Sep 14 (Print Publication: 2024).
DOI:	10.1016/j.isci.2024.110956
Abstrakt:	Parkinson's disease (PD) involves complex molecular interactions and diverse comorbidities. To better understand its molecular mechanisms, we employed systems medicine approaches using the PD map, a detailed repository of PD-related interactions and applied Probabilistic Boolean Networks (PBNs) to capture the stochastic nature of molecular dynamics. By integrating cohort-level and real-world patient data, we modeled PD's subtype-specific pathway deregulations, providing a refined representation of its molecular landscape. Our study identifies key regulatory biomolecules and pathways that vary across PD subtypes, offering insights into the disease's progression and patient stratification. These findings have significant implications for the development of targeted therapeutic interventions. Competing Interests: The authors declare no competing interests. (© 2024 The Author(s).)
Databáze:	MEDLINE
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