A network inference method for large-scale unsupervised identification of novel drug-drug interactions

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process

Missing and spurious interactions and the reconstruction of complex networks

Network analysis is currently used in a myriad of contexts: from identifying potential drug targets to predicting the spread of epidemics and designing vaccination strategies, and from finding friends to uncovering criminal activity. Despite the promise of the network approach, the reliability of network data is a source of great concern in all fields where complex networks are studied. Here, we present a general mathematical and computational framework to deal with the problem of data reliability in complex networks. In particular, we are able to reliably identify both missing and spurious interactions in noisy network observations. Remarkably, our approach also enables us to obtain, from those noisy observations, network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves. Our approach has the potential to guide experiments, to better characterize network data sets, and to drive new discoveries

Predicting human preferences using the block structure of complex social networks

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups

Modularity from Fluctuations in Random Graphs and Complex Networks

The mechanisms by which modularity emerges in complex networks are not well understood but recent reports have suggested that modularity may arise from evolutionary selection. We show that finding the modularity of a network is analogous to finding the ground-state energy of a spin system. Moreover, we demonstrate that, due to fluctuations, stochastic network models give rise to modular networks. Specifically, we show both numerically and analytically that random graphs and scale-free networks have modularity. We argue that this fact must be taken into consideration to define statistically-significant modularity in complex networks.Comment: 4 page

Agressivitat i adolescència : un problema social?

La preocupació per l'agressivitat a les escoles de secundària ha augmentat considerablement durant els darrers anys. En aquest article, fonamentat en una recerca empírica duta a terme en instituts de secundària, intentem proporcionar eines per comprendre el fenomen de l'agressivitat com a element intrínsec en les relacions socials quotidianes a l'adolescència. Analitzem com l'agressivitat s'estructura socialment d'una manera concreta segons la localització social i cultural, especialment a partir del gènere i la classe social. També aprofundim en l'agressivitat a partir de tres dimensions: 1) les jerarquies establertes a l'escola entre els joves; 2) la segregació entre nois i noies i els seus mons de vida, i 3) la importància del control i l'autocontrol.La preocupación por la agresividad en los centros de secundaria ha aumentado considerablemente durante los últimos años. En este artículo, fomentado en base a una investigación empírica llevada a cabo en institutos de secundaria, intentamos proporcionar herramientas para comprender el fenómeno de la agresividad como elemento intrínseco en las relaciones sociales cotidianas de la adolescencia. Analizamos como la agresividad se estructura socialmente de una forma concreta según la localización social y cultural, especialmente a partir del género y la clase social. También profundizamos en la agresividad a partir de tres dimensiones: 1) las jerarquías establecidas en la escuela entre los jóvenes; 2) la segregación entre chicos y chicas y sus mundos de vida, y 3) la importancia del control y autocontrol.In Catalonia (Spain) there is a growing concern about agressiveness in secondary schools. In this article, based on an empirical research carried out in secondary schools, we try to provide tools to understand agressiveness as an inherent element of everyday social relations in adolescence. We analyze how agressiveness is socially structured depending on social and cultural position, particularly in relation to gender and social class. We also analyze agressiveness from three different dimensions: 1) school hierarchical pecking-order; 2) segregation between boys and girls and their life worlds; 3) the importance of control and self-control