Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Fusing literature and full network data improves disease similarity computation.

Abstract: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

Significance of Clustering Coefficient over Jaccard Index

Abstract: Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information network and citation network to predict the future links. Every network has got certain structural properties. For predicting the accuracy of future links, structural properties might play an important role. Current work is an effort to find out the correlation between structural properties of network and prediction accuracy. Positive Correlation will signify that knowing the structural properties of a Network will assist us to know the prediction accuracy in advance.

Macro-economic time series modeling and interaction networks

Abstract: Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisions. In this contribution we describe an approach based on genetic programming and symbolic regression to identify variable interactions in large datasets. In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models. The result is a variable interaction network that describes which variables are most relevant for the approximation of each variable of the dataset. This approach is applied to a macro-economic dataset with monthly observations of important economic indicators in order to identify potentially interesting dependencies of these indicators. The resulting interaction network of macro-economic indicators is briefly discussed and two of the identified models are presented in detail. The two models approximate the help wanted index and the CPI inflation in the US.

The Correlation Fractal Dimension of Complex Networks

Abstract: The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

Using Information Content for Expanding Human Protein Coding Gene Interaction Networks

Abstract: Molecular interaction networks have emerged as central analysis concept for Omics profile interpretation. This fact is driven by the need for improving hypothesis generation beyond the mere interpretation of molecular feature lists derived from statistical analysis of high throughput experiments. A number of human gene and protein interaction networks are available for such task, but these differ with respect to biological nature of interactions represented, and vary with respect to coverage of molecular feature space on the gene, transcript, protein and metabolite level. Naturally, both elements impose major impact on hypothesis generation. We here present a methodology for deriving expanded interaction networks via consolidating available interaction information and further adding computationally inferred interactions. Integrating interaction data as provided in the public domain repositories IntAct, BioGrid and Reactome resulted in a core interaction network representing 11,162 human protein coding genes (out of a total of 19,980 protein coding genes) and 145,391 interactions. Utilizing annotation from ontologies on involvement in specific molecular pathways and function, combined with structural (domain) information as gene/protein node parameterization allowed computation of probabilities for additional interactions resting on the information content of individual sources. Utilizing topological information as degree centrality, global clustering coefficient and characteristic path length allowed defining a cutoff for interaction probabilities, resulting in an expanded interaction network holding 13,730 protein coding genes and 830,470 interactions. Evaluating such hybrid network against established interaction networks as KEGG showed significant recovery of evident interactions, indicating the validity of the expansion methodology. Integrating available interaction data, further enlarged by inferred interactions, provided an expanded human interactome regarding both, number of represented molecular features as well as number of interactions, thereby promising improved Omics profile interpretation.