The National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering.
The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE.
The project will also focus on user-centered development and pilot testing of myAURA to validate if and how it improves patient activation through a series of studies lead by Dr. Wendy Miller, Assistant Professor at the IU School of Nursing. “Our previous work on depression showed that it’s feasible to look at social media for health-related issues,” Rocha said. “The collaboration with Dr. Miller, who is an expert in epilepsy management, will allow us to translate our data and network science research, and develop personalized inferences useful to patients of this chronic disease.”
“On the last Friday of each month, instead of heading home to their families after the weekly School of Informatics, Computing and Engineering faculty meeting, professors Luis Rocha and Johan Bollen head to the Root Cellar Lounge and become DJ E-Trash and DJ Angst. […] Both Bollen and Rocha are considered experts in the field of complex networks and systems, and they agree that when they DJ, they are part of just the kind of complex systems they study”. See full article at IU News.
‘Musical Morphogenesis’ is an interactive installation that translates to sound, movement and lights the dynamics behind the development of petals in a flower. It is a collaborative piece developed by designers, architects, musicians and scientists in Luis Rocha‘s CASCI group. The control of this robotic “macroscope” is an implementation of the gene regulatory network of the Thaliana Arabidopsis flower. The installation provides a sensorial exploration of the dynamics between genes and proteins that leads to organ formation in plants, namely sepals, stamen, and petals. The genetic network provides an interactive “genetic soundtrack” that allows visitors to control the development of the plant towards its wild-type or mutant states. The installation has been on display at various museums such as the Science Museum in Lisbon, Gulbenkian Foundation, and the Belém ArtFest. This week it will be displayed at Dia D Ligações – Gulbenkian Foundation.
Sponsored by Persistent Systems. Luis Rocha, Director of the Complex Systems PhD track in the School of Information and Computing at Indiana University Bloomington, explains the new software-driven approach to medical research. Big data generated through social media such as Twitter and Instragram provides a far deeper and fuller examination of the impact of medicines and diseases, leading to greater actionable insights to improve the efficacy of prevention and treatment.
Network science has allowed us to understand the organization of complex systems across disciplines. However, there is a need to understand how to control them; for example, to identify strategies to revert a diseased cell to a healthy state in cancer treatment. Recent work in the field—based on linear control theory—suggests that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics. Such graph-based approaches have been used, for instance, to suggest that biological systems are harder to control and have appreciably different control profiles than social or technological systems. The methodology has also been increasingly used in many applications from financial to biochemical networks.
In work published today in Nature Scientific Reports, CNetS graduate student Alexander Gates and Professor Luis Rocha demonstrate that such graph-based methods fail to characterize controllability when dynamics are introduced. The study computed the control profiles of large ensembles of multivariate systems as well as existing Systems Biology models of biochemical regulation in various organisms.