title: A network-based feature selection approach to identify metabolic signatures in disease.
abstract: The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.
authors: Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tyrol, Austria. michael.netzer@umit.at
authors: Michael
authors: M
authors: Netzer
authors: Karl G
authors: KG
authors: Kugler
authors: Laurin A J
authors: LA
authors: Müller
authors: Klaus M
authors: KM
authors: Weinberger
authors: Armin
authors: A
authors: Graber
authors: Christian
authors: C
authors: Baumgartner
authors: Matthias
authors: M
authors: Dehmer
mesh_terms: D000328:Adult; D000465:Algorithms; D016022:Case-Control Studies; D006801:Humans; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000378:metabolism*