Reference : Computational systems biology approaches for Parkinson's disease
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Multidisciplinary, general & others
Human health sciences : Neurology
Systems Biomedicine
http://hdl.handle.net/10993/32946
Computational systems biology approaches for Parkinson's disease
English
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
In press
Cell & Tissue Research
Springer Science & Business Media B.V.
Parkinson's disease: Molecules, cells, and circuitries
Yes (verified by ORBilu)
International
0302-766X
1432-0878
New York
NY
[en] Parkinson's disease ; systems biology ; bioinformatics ; pathway analysis ; network analysis ; computational ; statistics ; cross-disease comparison ; biomarker ; diagnostics ; machine learning
[en] Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions.
This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/32946
10.1007/s00441-017-2734-5
A link to the publisher's original version of the article will be added here once available.
FnR ; FNR7643563 > Rudi Balling > Mito-PD > Mitochondrial Endophenotypes Of Pd > 01/01/2015 > 31/12/2017 > 2013

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