Category Archives: Rocha

CNETS Team Uses Instagram to monitor Drug Interactions and Adverse Reactions

Subnetwork of symptoms and drugs associated with Psoriasis and Epilepsy
Subnetwork of symptoms and drugs associated with Psoriasis and Epilepsy

Update: On March 21st, 2016 the paper described below (PMC4720984) was highlighted by Russ Altman from Stanford University in his yearly review as one of 30 important papers of the year in translational bioinformatics.

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.

Continue reading CNETS Team Uses Instagram to monitor Drug Interactions and Adverse Reactions

CNetS team studies generalized modularity in complex networks & Systems

mediumModularity 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.

Awards at CCS 2015

Optimized-IU_poster_5_botsThe 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).

The paper “Modularity and the Spread of Perturbations in Complex Dynamical Systems” by Artemy Kolchinsky, Alexander J. Gates and Luis M. Rocha, and the poster “Information Theoretic Structures of the French Revolution” by Alexander Barron, Simon DeDeo and Rebecca Spang won additional awards.

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).

Congratulations to the CNetS team!

 

New CASCI papers on Complex Networks

Network Science Journal
Network Science

Read new papers from CASCI on developing the mathematical toolbox available to deal with computing distances on weighted graphs, applying distance closures for computational fact checking, and computing multi-scale integration in brain networks:

T. Simas and L.M. Rocha [2015].”Distance Closures on Complex Networks”. Network Science, doi:10.1017/nws.2015.11.

G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini [2015]. “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 [2014]. “Multi-scale Integration and Predictability in Resting State Brain Activity”. Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.

NIH Project to study Drug-Drug Interaction


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.

Canalization and Control in Automata Networks

Dynamical Modularity
Dynamical Modularity

Read our latest paper titled Canalization and Control in Automata Networks: Body Segmentation in Drosophila melanogaster in PLoS ONE. Authors Manuel Marques-Pita & Luis Rocha ask, How do cells and tissues ‘compute’? Schema redescription is presented as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. Canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). Several new results ensue from this methodology developed as part of the CASCI collective dynamics project: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. The methodology is applicable to any complex network that can be modelled using automata, but this work focuses on biochemical regulation and signalling, with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster.