Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. Network Medicine introduces this rapidly evolving field of medical research, which promises to revolutionize the diagnosis and treatment of human diseases. With contributions from leading experts that highlight the necessity of a team-based approach in network medicine, this definitive volume provides readers with a state-of-the-art synthesis of the progress being made and the challenges that remain.
Medical researchers have long sought to identify single molecular defects that cause diseases, with the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the inherent complexity of human diseases and has often led to treatments that are inadequate or fraught with adverse side effects. Rather than trying to force disease pathogenesis into a reductionist model, network medicine embraces the complexity of multiple influences on disease and relies on many different types of networks: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples. The authors offer a systematic approach to understanding complex diseases while explaining network medicine’s unique features, including the application of modern genomics technologies, biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in an integrative context.
By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease.
This article describes the themes found in the past 25 years of creativity research. Computational methods and network analysis were used to map keyword theme development across ~1,400 documents and ~5,000 unique keywords from 1990 (the first year keywords are available in Web of Science) to 2015.
Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years Rich Williams, Mark A. Runco & Eric Berlow Creativity Research Journal.Volume 28, 2016 - Issue 4 Pages 385-394 | Published online: 14 Nov 2016
Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.
Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction Geoff Boeing
Systems 2016, 4(4), 37; doi:10.3390/systems4040037
Alison Gopnik is the author of the New York Times best seller The Philosophical Baby, a regular contributor to The Wall Street Journal, and a pioneer in developmental psychology and understanding the way children learn. Gopnik’s new book, The Gardener and the Carpenter—which came out in August 2016—addresses the growing pressure on parents and teachers to ensure that children develop in one particular way.
That’s a losing strategy, insists Gopnik. Arguing passionately for a messier, less directed form of guidance for children, Gopnik summons evidence from decades of research that suggests that young kids are born learners—diverse, wildly unpredictable, easily distracted, but always processing information, cracking codes, and experimenting with new, innovative ideas that drive the species forward.
In this important article from NPQ’s EDI series, two researchers raise important questions every nonprofit and philanthropy should consider in the way nonprofits design research and use data. Let’s talk about it!
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.
An inspiring quote is a great tool for changing a mindset. I made a personal collection of 50 outstanding quotes on change and innovation. Use them to inspire others to start a culture of change, to think different and to prioritize change and innovation at the start of 2017.
The primary shift in the meetings and events industry in 2017 is going to revolve around delivering business events that engage attendees in more multidisciplinary ways.
For the last five years, the industry has been focused on two trends impacting meeting design strategy above all else: the rise of event technology and the emergence of the Millennial generation. There was growing consensus in 2016, however, that it’s time for the meetings industry to move beyond its preoccupation with those themes.
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Hidden geometric correlations in real multiplex networks Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos Nature Physics 12, 1076–1081 (2016) doi:10.1038/nphys3812
Innovation continues to be a hot topic. The Boston Consulting Group’s 10th annual global survey of the state of innovation shows that 79 percent of respondents ranked it as the company’s top-most priority or a top-three priority—the highest percent since the survey began in 2005. Whether in business, non-profit, sports, or entertainment, most organizations are […]
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