title: A network-based approach to classify the three domains of life.
abstract: BACKGROUND
  Identifying group-specific characteristics in metabolic networks can provide better insight into evolutionary developments. Here, we present an approach to classify the three domains of life using topological information about the underlying metabolic networks. These networks have been shown to share domain-independent structural similarities, which pose a special challenge for our endeavour. We quantify specific structural information by using topological network descriptors to classify this set of metabolic networks. Such measures quantify the structural complexity of the underlying networks. In this study, we use such measures to capture domain-specific structural features of the metabolic networks to classify the data set. So far, it has been a challenging undertaking to examine what kind of structural complexity such measures do detect. In this paper, we apply two groups of topological network descriptors to metabolic networks and evaluate their classification performance. Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance.
  
  
  RESULTS
  By combining the two groups, we can identify seven topological network descriptors that show a group-specific characteristic by ANOVA. A multivariate analysis using feature selection and supervised machine learning leads to a reasonable classification performance with a weighted F-score of 83.7% and an accuracy of 83.9%. We further demonstrate that our approach outperforms alternative methods. Also, our results reveal that entropy-based descriptors show the highest classification ability for this set of networks.
  
  
  CONCLUSIONS
  Our results show that these particular topological network descriptors are able to capture domain-specific structural characteristics for classifying metabolic networks between the three domains of life.
authors: Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), Austria.
authors: Laurin A J
authors: LA
authors: Mueller
authors: Karl G
authors: KG
authors: Kugler
authors: Michael
authors: M
authors: Netzer
authors: Armin
authors: A
authors: Graber
authors: Matthias
authors: M
authors: Dehmer
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001105:Archaea / Q000145:classification* / Q000378:metabolism; D001185:Artificial Intelligence; D001419:Bacteria / Q000145:classification* / Q000378:metabolism; D003196:Computer Graphics; D056890:Eukaryota / Q000145:classification* / Q000378:metabolism; D016015:Logistic Models; D053858:Metabolic Networks and Pathways; D015203:Reproducibility of Results; D012984:Software