Dedalo: looking for clusters explanations in a labyrinth of Linked Data

We present Dedalo, a framework which is able to exploit Linked Data to generate explanations for clusters. In general, any result of a Knowledge Discovery process, including clusters, is interpreted by human experts who use their background knowledge to explain them. However, for someone without such expert knowledge, those results may be difficult to understand. Obtaining a complete and satisfactory explanation becomes a laborious and time-consuming process, involving expertise in possibly different domains. Having said so, not only does the Web of Data contain vast amounts of such background knowledge, but it also natively connects those domains. While the efforts put in the interpretation process can be reduced with the support of Linked Data, how to automatically access the right piece of knowledge in such a big space remains an issue. Dedalo is a framework that dynamically traverses Linked Data to find commonalities that form explanations for items of a cluster. We have developed different strategies (or heuristics) to guide this traversal, reducing the time to get the best explanation. In our experiments, we compare those strategies and demonstrate that Dedalo finds relevant and sophisticated Linked Data explanations from different areas

Connections between Classical and Parametric Network Entropies

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity

Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level

New Polynomial-Based Molecular Descriptors with Low Degeneracy

In this paper, we introduce a novel graph polynomial called the ‘information polynomial’ of a graph. This graph polynomial can be derived by using a probability distribution of the vertex set. By using the zeros of the obtained polynomial, we additionally define some novel spectral descriptors. Compared with those based on computing the ordinary characteristic polynomial of a graph, we perform a numerical study using real chemical databases. We obtain that the novel descriptors do have a high discrimination power

Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure

Influenza Virus Non-Structural Protein 1 (NS1) Disrupts Interferon Signaling

Type I interferons (IFNs) function as the first line of defense against viral infections by modulating cell growth, establishing an antiviral state and influencing the activation of various immune cells. Viruses such as influenza have developed mechanisms to evade this defense mechanism and during infection with influenza A viruses, the non-structural protein 1 (NS1) encoded by the virus genome suppresses induction of IFNs-α/β. Here we show that expression of avian H5N1 NS1 in HeLa cells leads to a block in IFN signaling. H5N1 NS1 reduces IFN-inducible tyrosine phosphorylation of STAT1, STAT2 and STAT3 and inhibits the nuclear translocation of phospho-STAT2 and the formation of IFN-inducible STAT1:1-, STAT1:3- and STAT3:3- DNA complexes. Inhibition of IFN-inducible STAT signaling by NS1 in HeLa cells is, in part, a consequence of NS1-mediated inhibition of expression of the IFN receptor subunit, IFNAR1. In support of this NS1-mediated inhibition, we observed a reduction in expression of ifnar1 in ex vivo human non-tumor lung tissues infected with H5N1 and H1N1 viruses. Moreover, H1N1 and H5N1 virus infection of human monocyte-derived macrophages led to inhibition of both ifnar1 and ifnar2 expression. In addition, NS1 expression induces up-regulation of the JAK/STAT inhibitors, SOCS1 and SOCS3. By contrast, treatment of ex vivo human lung tissues with IFN-α results in the up-regulation of a number of IFN-stimulated genes and inhibits both H5N1 and H1N1 virus replication. The data suggest that NS1 can directly interfere with IFN signaling to enhance viral replication, but that treatment with IFN can nevertheless override these inhibitory effects to block H5N1 and H1N1 virus infections

Biological characteristics of a cold-adapted influenza A virus mutation residing on a polymerase gene

The biological function of a cold-adapted (ca) mutation residing on the PB2 gene of an influenza A/Ann Arbor/6/60 (A/AA/6/60) ca variant virus in the viral replication cycle at 25° C was studied. The viral polypeptide synthesis of A/AA/6/60 ca variant at 25° C was evident approximately 6 hours earlier than the wild type (wt) virus and yielded twice as many products. The quantitative analysis of viral complementary RNA (cRNA), synthesized in the presence of cycloheximide, revealed that A/AA/6/60 ca variant and a single gene reassortant that contains only the PB2 gene of the ca variant with remaining genes of the wt virus produced equal amount of cRNA at 25° and 33° C, which was an amount approximately four fold greater than the wt virus' cRNA synthesized at 25° C. These results strongly suggest that the ca mutation residing on the PB2 gene of A/AA/6/60 ca variant affects the messenger RNA synthesis at 25° C in the primary transcription.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41692/1/705_2005_Article_BF01310893.pd

Internet of things: where to be is to trust

[EN] Networks' creation is getting more and more required, anytime, anywhere. Devices that can participate on these networks can be quite different among them. Sensors, mobiles, home appliances, or other type of devices will have to collaborate to increase and improve the services provided to clients. In the same way, network configuration, security mechanisms establishment, and optimal performance control must be done by them. Some of these devices could have limited resources to work, sometimes even resources restriction not existing, they must work to optimize network traffic. In this article, we center our researching on spontaneous networks. We propose a secure spontaneous ad-hoc network, based on direct peer-to-peer interaction and communities' creation to grant a quick, easy, and secure access to users to surf the Web. Each device will have an identity in the network. Each community will also have an identity and will act as a unity on a world based on Internet connection. 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