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Archive for the tag “networks”

Network medicine startups

There are two (well, I’m sure there are really more) interesting new startups that combine medicine with networks, albeit in different ways. NuMedii (which appears to be shorthand for New Indications of Medicines) uses a data-driven approach to discover new indications for previously existing drugs. This is potentially very useful because existing drugs have gone through rigorous tests for toxicity etc. and are therefore easier to bring to the market rather than developing a drug from scratch. NuMedii’s technology is based on academic work from Stanford and they have a killer team that includes the likes of Atul Butte and Eric Schadt. The company is currently looking for what is essentially a bioinformatics-slanted big data scientist; one of the responsibilities related to this position is to “Architect, develop, maintain, and document a computational infrastructure that efficiently executes complex queries across many terabytes (potentially petabytes!) of disparate data and knowledge on genomics, genetics, pharmaceuticals, and chemicals.” Petabytes!

MedNetworks is also interesting, though a bit different. Its technology is based on the well-publicized work of Nicholas Christakis and colleagues at Harvard about how things like smoking and obesity appear to spread in social networks in an almost contagious way. (As an aside, I saw a random hipster at a Stockholm café sporting a copy of Christakis’ and Fowler’s book Connected: The Surprising Power of Out Social Networks – maybe network science is belatedly going mainstream here too!) MedNetworks studies things like how prescriptions of drugs are affected by the structures of social networks of physicians and patients. They attempt to identify “high influencers” in social networks, which is not necessarily the same as highly connected people. These high influencers have a strong influence on how drug prescribing behavior “diffuses” in a social network. Quoting the company website: “Optimized targeting for promotion based on social network influence provides a more efficient and effective approach to both personal and non-personal promotion.”


From social atoms to aggregates

In a recent Perspectives article in Science (subscription required, unfortunately), Alessandro Vespignani lays out a research program that recalls Hari Seldon, the master statistician in Asimov’s Foundation series who has managed to develop a discipline called psychohistory, with which he can predict the future in probabilistic terms.

A huge flow of quantitative data that combine the demographic and behavioral aspects of society with the infrastructural substrate is becoming available […]. Analogously to what happened in physics, we are finally in the position to move from the analysis of the “social atom” or “social molecules” (i.e., small social groups) to the quantitative analysis of social aggregate states, as envisioned by social scientists at the beginning of the past century […]. Here, I refer to “social aggregate states” as large-scale social systems consisting of millions of individuals that can be characterized in space (geographic and social) and time. The shift from the study of a small number of elements to the study of the behavior of large-scale aggregates is equivalent to the shift from atomic and molecular physics to the physics of matter.

Vespignani goes on to briefly discuss reality mining, multiscale modelling and what he calls “network thinking”. He argues that if we succeed in ” […] the gathering of large-scale data on information spread and social reactions that occur during periods of crisis”,  ” […] the formulation of formal models that make it possible to quantify the effect of risk perception and awareness phenomena of individuals on the techno-social network structure and dynamics.” and ” […] the deployment of monitoring infrastructures capable of informing computational models in real time.”, we can

imagine the creation of computational forecasting infrastructures that will help us design better energy-distribution systems, plan for traffic-free cities, anticipate the demands of Internet connectivity, or manage the deployment of resources during health emergencies.

The article is part of a special issue on “Complex systems and networks“, and there is additional interesting material there about  econophysics, meta-network analysis, scale-free networks and other topics.

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