Integrative Data Science Lab

LINKING AND LEARNING FROM DATA

The Integrative Data Science Lab (IDSL) is housed in Indiana University's School of Informatics, Computing, and Engineering, and is run by David Wild. Integrative Data Science (IDS) brings together diverse data sets, technologies and expertise with linked data, machine learning and data science approaches to solve real world problems. We are developing technical infrastructures for integrative data science such as linked data ecosystems and heterogeneous graph analytic algorithms, as well as methodological approaches for complex problem solving.

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IDS for drug discovery

Accelerating drug discovery and reducing clinical trails failures

We are pioneering new ways to accelerate drug discovery, including making better decisions earlier, avoiding expensive failures in clinical trails, and finding the right treatments for the right patients. We developed Chem2Bio2RDF, the first large scale semantic linked data repository for preclinical drug discovery, novel link prediction and data mining algorithms for finding hidden insights in large heterogeneous data networks, and in our 2012 Drug Discovery Today paper laid out a strategy for using linked data and graph analytics to avoid some of the perils of single-target drug discovery.  Current projects include researching knowledge networks that encode computable networks for multi-mechanism complex diseases, integrating patient medical records with molecular data to help identify potential targeted therapies, and developing new ways to apply machine learning on top of massive heterogeneous linked data structures. We are thankful to NIH NCATS, Indiana CTSI, the OpenPHACTS foundation, Eli Lilly, and Pfizer for funding of this work. Applications in this area are being commercialized in our company Data2Discovery Inc.

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IDS for emergency response & management

Improving outcomes in disasters and emergency response by smart integration of unconventional datasets, expertise and technologies

We live in an era of profound risk and uncertainty, with climate change, healthcare challenges, pervasive technology, infrastructure vulnerabilities, and cybersecurity all creating new and enhanced threats at local, regional and national levels. We are researching highly creative ways that data from unconventional sources and low cost technologies can be used together to help emergency managers, emergency responders and citizens better understand, prepare for, mitigate and respond to this new landscape. We are re-imagining situational awareness, emergency protocols and resource planning in a world awash with data and technology. We are doing this this through strong partnerships at the local, regional and national level with city government, fire, police and EMS departments and data providers.

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IDS for regional economic development

Identifying precision policy changes through data science

Through a $1.4m grant from the Economic Development Agency (EDA) and in collaboration with the Center for Complex Networks and Systems Research and the Indiana Business Research Center at IU, we are researching how Data Science, Complex Adaptive Systems and Social Sciences can come together to identify precision policy changes to promote regional economic development. We are doing this through a linked data ecosystem that maps together non-traditional and unconventional datasets, which are used to create "unconventional descriptors" that can be used in existing economic models.