Title |
A network-based approach to classify the three domains of life
|
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Published in |
Biology Direct, October 2011
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DOI | 10.1186/1745-6150-6-53 |
Pubmed ID | |
Authors |
Laurin AJ Mueller, Karl G Kugler, Michael Netzer, Armin Graber, Matthias Dehmer |
Abstract |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Denmark | 1 | 3% |
China | 1 | 3% |
Germany | 1 | 3% |
Unknown | 28 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 19% |
Student > Master | 5 | 16% |
Researcher | 4 | 13% |
Professor | 4 | 13% |
Student > Postgraduate | 3 | 9% |
Other | 6 | 19% |
Unknown | 4 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 12 | 38% |
Computer Science | 4 | 13% |
Arts and Humanities | 2 | 6% |
Medicine and Dentistry | 2 | 6% |
Mathematics | 1 | 3% |
Other | 6 | 19% |
Unknown | 5 | 16% |