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.
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.
The project “Social SLAM: Creating Dynamical Socio-Environmental Models for Mobile Robots”, a collaboration between Luis Rocha, Selma Sabanovic, Matt Francisco, and Alin Cosmanescu, has received an IUCRG grant for 2013-2014 from the Office of the Vice President for Research at Indiana University.
The pharmacokinetics ontology and corpus for text mining developed in collaboration with Li’s lab at IUPUI, part of CASCI Biomedical Literature Mining work, has been reported in BMC Bioinformatics where it has become a Highly Accessed paper:
University and industry scientists are determining how to forecast significant societal events, ranging from violent protests to nationwide credit-rate crashes, by analyzing the billions of pieces of information in the ocean of public communications, such as tweets, web queries, oil prices, and daily stock market activity.
“We are automating the generation of alerts, so that intelligence analysts can focus on interpreting the discoveries rather than on the mechanics of integrating information,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the computer science department at Virginia Tech. He is leading the team of computer scientists and subject-matter experts from Virginia Tech, the University of Maryland, Cornell University, Children’s Hospital of Boston, San Diego State University, University of California at San Diego, and Indiana University, and from the companies, CACI International Inc., and Basis Technology.
CNetS Professors Bollen and Rocha from the School of Informatics and Computing at Indiana University are members of this project. Prof. Bollen, has devised a way to evaluate the tone of tweets – calm, alert, vital, etc. — to predict stock market trends. Prof. Rocha, has developed bio-inspired methods to predict associations in biochemical, social, and knowledge networks, including web and e-mail systems.