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

Order-parameter fluctuations (OPF) in spin glasses: Monte Carlo simulations and exact results for small sizes

The use of parameters measuring order-parameter fluctuations (OPF) has been encouraged by the recent results reported in \cite{RS} which show that two of these parameters, $G$ and $G_c$, take universal values in the $\lim_{T\to 0}$. In this paper we present a detailed study of parameters measuring OPF for two mean-field models with and without time-reversal symmetry which exhibit different patterns of replica symmetry breaking below the transition: the Sherrington-Kirkpatrick model with and without a field and the Ising p-spin glass (p=3). We give numerical results and analyze the consequences which replica equivalence imposes on these models in the infinite volume. We give evidence for the transition in each system and discuss the character of finite-size effects. Furthermore, a comparative study between this new family of parameters and the usual Binder cumulant analysis shows what kind of new information can be extracted from the finite $T$ behavior of these quantities. The two main outcomes of this work are: 1) Parameters measuring OPF give better estimates than the Binder cumulant for $T_c$ and even for very small systems they give evidence for the transition. 2) For systems with no time-reversal symmetry, parameters defined in terms of connected quantities are the proper ones to look at.Comment: 23 pages, REVTeX, 11 eps figure

Order-parameter fluctuations in Ising spin glasses at low temperatures

We present a numerical study of the order-parameter fluctuations for Ising spin glasses in three and four dimensions at very low temperatures and without an external field. Accurate measurements of two previously introduced parameters, A and G, show that the order parameter is not self-averaging, consistent with a zero-temperature thermal exponent value \theta' \simeq 0, and confirm the validity of the relation G=1/3 in the thermodynamic limit in the whole low-temperature phase, as predicted by stochastic stability arguments.Comment: 7 pages, 7 eps figures, RevTe

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

Detection of node group membership in networks with group overlap

Most networks found in social and biochemical systems have modular structures. An important question prompted by the modularity of these networks is whether nodes can be said to belong to a single group. If they cannot, we would need to consider the role of "overlapping communities." Despite some efforts in this direction, the problem of detecting overlapping groups remains unsolved because there is neither a formal definition of overlapping community, nor an ensemble of networks with which to test the performance of group detection algorithms when nodes can belong to more than one group. Here, we introduce an ensemble of networks with overlapping groups. We then apply three group identification methods--modularity maximization, k-clique percolation, and modularity-landscape surveying--to these networks. We find that the modularity-landscape surveying method is the only one able to detect heterogeneities in node memberships, and that those heterogeneities are only detectable when the overlap is small. Surprisingly, we find that the k-clique percolation method is unable to detect node membership for the overlapping case.Comment: 12 pages, 6 figures. To appear in Euro. Phys. J

Dinámica estacional y producción anual de las comunidades dominadas por Cystoseira Crinita (Fucales: Ochrophyta) del Mediterráneo noroccidental

Algae of the genus Cystoseira are the main engineering species on Mediterranean shallow rocky bottoms. Cystoseira crinita is an endemic species which grows in shallow and rather sheltered environments throughout the entire Mediterranean Sea. In order to investigate its role in structuring benthic assemblages and as a primary producer, three localities were sampled every two months during one year in Menorca (Balearic Islands). The total biomass of Cystoseira crinita-dominated assemblages showed a seasonal pattern mainly due to temporal changes in the biomass of the dominant alga. The assemblages also showed seasonality in their species richness (number of species per sample). Both total biomass and species richness peaked in summer, and their lowest values were recorded in winter. Despite these temporal patterns, C. crinita-dominated assemblages from Menorca showed reduced seasonality compared to C. crinita-dominated assemblages in other areas in the western Mediterranean, as C. crinita specimens kept their branches throughout the entire year. Total annual production of Cystoseira crinita branches and cauloids was around 1230 g dwt m–2, which is higher than that of other Cystoseira species living in sheltered areas but much lower than that of Cystoseira species growing on exposed shores. Production was highly seasonal, and was highest in spring and null in winter and late summer.Las algas del género Cystoseira son las principales especies estructuradoras de hábitat en los fondos rocosos infralitorales mediterráneos. Cystoseira crinita es una especie endémica que crece en fondos someros y poco expuestos al oleaje en todo el Mediterráneo. Con la intención de estudiar su papel estructurador en las comunidades bentónicas y como productor primario, se muestrearon tres localidades, cada dos meses durante un año, en Menorca, Islas Baleares. Las comunidades dominadas por Cystoseira crinita mostraron un ciclo anual bien establecido en su biomasa total, debido principalmente a los cambios temporales de biomasa del alga Cystoseira crinita. Las comunidades también mostraron cambios en su riqueza específica (número de especies por muestra). Tanto la biomasa total como la riqueza específica fueron máximas en verano, mientras sus valores mínimos se obtuvieron en invierno. A pesar de estos patrones temporales, las comunidades de C. crinita estudiadas mostraron una estacionalidad menor que la encontrada en otras zonas, puesto que C. crinita mantuvo sus rámulos durante todo el año. La producción total anual de los rámulos y cauloides de Cystoseira crinita fue de 1230 g peso seco m–2, más elevada que la de otras especies de Cystoseira de modo calmo, pero muy inferior a las medidas obtenidas en especies de Cystoseira que crecen en lugares expuestos. La producción fue marcadamente estacional, con máximos en primavera y con valores nulos en invierno y final de verano

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