Reference : Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/46639
Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.
English
Badkas, Apurva mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Nguyen, Thanh-Phuong mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit]
Caberlotto, Laura [> >]
Schneider, Jochen mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Medical Translational Research]
De Landtsheer, Sébastien [> >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
2021
Biology
10
2
Yes (verified by ORBilu)
International
2079-7737
Switzerland
[en] co-morbidities ; metabolic disease genes ; metabolic diseases ; networks ; topology
[en] A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.
Researchers
http://hdl.handle.net/10993/46639
FnR ; FNR9139104 > Thanh Phuong Nguyen > > An Integrative Systems Medicine Approach to Mapping Human Metabolic Diseases > 01/07/2015 > 31/03/2017 > 2014

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