HIGHLIGHTS
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...
Predicting future conflict between team-members with parameter-free models of social networks
Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to...
The impact of individual biases on consensus formation
Social groups of interacting agents display an ability to coordinate in the absence of a central authority, a phenomenon that has been recently amplified by the widespread availability of social networking technologies. Models of opinion formation in a population of agents have proven a very useful tool to investigate these...
OUR RESEARCH
Complex Systems
Cells, ecosystems and economies are examples of complex systems. In complex systems, individual components interact with each other, usually in nonlinear ways, giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction's topology, complex systems cannot be properly understood by just analyzing their constituent parts.
Data Science
Humans generate information at an unprecedented pace, with some estimates suggesting that, in a year, we now produce on the order of 10^21 bytes of data, millions of times the amount of information in all the books ever written. Processing this "data deluge", as some have called it, requires new tools and new approaches at the interface of statistics, statistical and machine learning, network theory and statistical physics.
Multidisciplinarity
Our goal is to push forward the boundaries of science. We are interested in addressing fundamental questions in all areas of science including natural, social and economic sciences. We put a special emphasis in the development of tools that aid scientific discovery through understanding and quantification of a specific phenomenon. To this end we have assembled a multidisciplinary team and have established solid collaborations with experts in biology, social sciences, ecology and economics.