Modeling multi-scale data via a network of networks.
Bioinformatics
; 38(9): 2544-2553, 2022 04 28.
Article
en En
| MEDLINE
| ID: mdl-35238343
ABSTRACT
MOTIVATION Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related entities represented by nodes in a higher-level (higher scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a 'network of networks' (NoNs) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e. than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. RESULTS:
For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important. AVAILABILITY AND IMPLEMENTATION The software and data are available at https//nd.edu/~cone/NoNs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Programas Informáticos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2022
Tipo del documento:
Article