From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Online Publication Date2013-04-08
Print Publication Date2013-12
Permanent link to this recordhttp://hdl.handle.net/10754/334511
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AbstractGrowth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.
CitationCannistraci CV, Alanis-Lobato G, Ravasi T (2013) From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci Rep 3. doi:10.1038/srep01613.
PubMed Central IDPMC3619147
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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-sa/3.0/
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The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid techniqueTheofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P. (Artificial Intelligence Review, Springer Nature, 2013-07-12) [Article]Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
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