Recent CASCIComplex Systems & Networks Phd program graduate Artemy Kolchinsky, is now a postdoc at the Santa Fe Institute. While at SFI, Kolchinsky is working with “David Wolpert on several projects related to optimal use of information and prediction. One is the problem of modeling and analyzing complicated dynamical systems that require large amounts of time and computational power to simulate. […] Another project investigates connections
between information processing and statistical physics. […] The two are [also] beginning to work on understanding why different social groups develop different organizations, whether the group is a prehistoric tribe or a business firm.” More details on the SFI update newsletter.
Congratulations to Luis Rocha, who has been awarded a Fulbright Scholarship devoted to developing Complex Systems methodologies for the Life Sciences. The 12-month sabbatical is to be pursued under the Fulbright program for educational exchanges between the United States and Portugal. It will focus on studying collective behavior and control in biochemical and social networks. The broader goal is to advance our ability to predict and control the dynamics of complex networks in three domain areas: biochemical, neurodyamic, and social systems. The scholarship will also be used to facilitate the design of a new doctoral program on complex networks and systems for the life sciences.
Using complex networks analysis and social media mining, CNETS researchers from the CASCI team have found that Instagram, a growing social media platform among teens, can be used “to uncover drug-drug interactions (DDI) and adverse drug reactions (ADR).” The work shows that this popular social media service is “a very powerful source of data with great promise in the public-health domain”. The study, “Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines,” supported by an R01 grant from the National Institutes of Health as well as a gift from Persistent Inc., was recently published and presented at the Pacific Symposium on Biocomputing (PSB 2016), in Hawaii. (PubMed, arXiv). The results are based on almost 7.000 user timelines associated with depression drugs which combined have 5+ million posts.
Modularity in complex systems can be observed in networks and across dynamical states, time scales, and in response to different kinds of perturbations. In a paper published in Physical Review E (Rapid Communication), Kolchinsky, Gates & Rocha propose a principled alternative to detecting communities in static and dynamical networks. The method demonstrates that standard modularity measures on static networks can be seen as a special case of measuring the spread of perturbations in dynamical systems. Thus, the new method offers a powerful tool for exploring the modular organization of complex dynamical systems.
The CNetS poster “The Rise of Social Bots in Online Social Networks” by Emilio Ferrara, Onur Varol, Prashant Shiralkar, Clayton Davis, Filippo Menczer, and Alessandro Flammini won a Best Poster Award at CCS 2015. The poster was presented by Clayton Davis. The results will also appear in the paper “The Rise of Social Bots” to be published in Comm. ACM (in press, preprint).
Finally, our former postdoctoral scientist Bruno Gonçalves (now tenured faculty member at Aix-Marseille Université) received a Junior Scientist Award from the Complex Systems Society for his contributions to the study of human social behavior from large-scale online attention and behavioral data. This is the second Junior Scientist Award for CNetS (the first was won by Filippo Radicchi).
G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini . “Computational fact checking from knowledge networks.” PLoS One. In Press. arXiv:1501.03471.
A. Kolchinsky, M. P. Van Den Heuvel, A. Griffa, P. Hagmann, L.M. Rocha, O. Sporns, J. Goni . “Multi-scale Integration and Predictability in Resting State Brain Activity”. Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.
Prof. Luis Rocha from CNETS at IU Bloomington, Prof. Lang Li from IUPUI Medical School, and Prof. Hagit Shatkay from the University of Delaware have been awarded a four-year, $1.7M grant from NIH/NLM to study the large-scale extraction of drug-Interaction from medical text. Drug-drug interaction (DDI) leads to adverse drug reactions, emergency room visits and hospitalization, thus posing a major challenge to public health. To circumvent risk to patients, and to expedite biomedical research, both clinicians and biologists must have access to all available knowledge about potential DDI, and understand both causes and consequences of such interactions. However, mere identification of interactions does not directly support such understanding, as evidence for DDI varies broadly, from reports of molecular interactions in basic-science journals, to clinical descriptions of adverse-effects in a myriad of medical publications. This project will develop tools that focus directly on large-scale identification and gathering of various types of reliable experimental evidence of DDI from diverse sources. The successful completion of the project will provide clinicians and biologists with substantiated knowledge about drug interactions and with informatics tools to obtain such information on a large-scale, laying the basis for preventing adverse drug reactions and for exploring alternative treatments.