Search

Community Detection is a fundamental task in the field of Social Network Analysis, extensively studied in literature. Recently, some approaches have been proposed to detect communities distinguishing their members between kernel that represents opinion leaders, and auxiliary who are not leaders but are linked to them. However, these approaches suffer from two important limitations: first, they cannot identify overlapping communities, which are often found in social networks (users are likely to belong to multiple groups simultaneously); second, they cannot deal with node attributes, which can provide important information related to community affiliation. In this paper we propose a method to improve a well-known kernel-based approach named Greedy-WeBA (Wang et al., 2011) and overcome these limitations. We perform a comparative analysis on three social network datasets, Wikipedia, Twitter and Facebook, showing that modeling overlapping communities and considering node attributes strongly improves the ability of detecting real social network communities.

International audience; The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but most of the existing approaches are based only on text, disregarding information that comes from the relationships among users and posts. In this paper we consider microblogs as heterogeneous networks and we use an approach based on latent representation of nodes to infer, given a specific topic, the sentiment polarity of posts and users at the same time. The experimental investigation show that our approach, by taking into account both content and relationship information, outperforms supervised classifiers based only on textual content.

In this paper authors perform a variability analysis of a Stochastic Petri Net (SPN) model of the Tissue Factor induced coagulation cascade, one of the most complex biochemical networks. This pathway has been widely analyzed in literature mostly with ordinary differential equations, outlining the general behaviour but without pointing out the intrinsic variability of the system. The SPN formalism can introduce uncertainty to capture this variability and, through computer simulation allows to generate analyzable time series, over a broad range of conditions, to characterize the trend of the main system molecules. We provide a useful tool for the development and management of several observational studies, potentially customizable for each patient. The SPN has been simulated using Tau-Leaping Stochastic Simulation Algorithm, and in order to simulate a large number of models, to test different scenarios, we perform them using High Performance Computing. We analyze different settings for model representing the cases of "healthy" and different " unhealthy" subjects, comparing and testing their variability in order to gain valuable biological insights. © 2013 Springer-Verlag.

Sentiment Analysis for polarity classification on microblogs is generally based on the assumption that texts are independent and identically distributed (i.i.d). Although these methods are aimed at handling the complex characteristics of natural language, usually they do not consider microblogs as networked data. Early approaches for overcoming this limitation consist in exploiting friendship relationships, since connected users may be more likely to hold similar opinions (Homophily and Social Influence). However, the assumption about the friendship relations does not reflect the real world, where two connected users could have different opinions about the same topic. In order to overcome these shortcomings, we propose a semi-supervised framework that estimates user polarities about a given topic by combining post contents and weighted approval relations, which are intended to better represent the contagion on social networks. The experimental investigation reveals that incorporating approval relations can lead to statistically significant improvements over the performance of complex supervised classifiers based only on textual features. © Springer International Publishing Switzerland 2013.

Modeling across multiple scales is a current challenge in Systems Biology, especially when applied to multicellular organisms. In this paper, we present an approach to model at different spatial scales, using the new concept of Hierarchically Colored Petri Nets (HCPN). We apply HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) signaling in Drosophila wing. We have constructed a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP. In addition our models include the effect of well-studied genetic mutations. We have applied a set of analytical techniques including clustering and model checking over time series of primary and secondary data. Our models support the interpretation of biological observations reported in the literature.

Oral anticoagulation therapy, largely performed by warfarin-based drugs, is commonly used for patients with a high risk of blood clotting which can lead to stroke or thrombosis. The state of the patient, with respect to anticoagulation, is captured by the index INR, which is to be kept within a therapeutic range. The patient’s response is marked by high inter-individual and inter-temporal variability, which can lead to serious adverse events. The Food and Drug Administration (FDA)’s Adverse Event Reporting System indicated that warfarin is among the 10 top drugsfor the adverse events reported during the 1990 and 2000 decades . In the last years genetic testing has been argued as a base for personalize dosing: indeed polymorphisms of two genes CYP2C9 and VKORC1 are markers of lower dosing requirements, but still account for a relatively minor part of this variability. For these reason there is no widely agreed conclusion as to whether the net economic results, keeping the cost of testing into account of the therapeutic improvement allowed by genetic testing are positive. In this paper the authors develop a model based on a decision tree to assess the cost-effectiveness of genotype based warfarin dosing. Clinical/therapeutical data inputs for the model have been derived from a database of nearly 4000 patients of which around 350 have been so far genotyped. Several scenarios have been simulated about the prevalence of major bleeding and ischemic events among anticoagulated patients and the rate of their reduction attributable, according to the literature, to genotyped dosing.