We are pioneering new ways to rapidly improve early-stage drug discovery using integrative knowledge graphs and advanced machine learning approaches to profile and predict the biological effects of potential new drugs
The world is already facing the impacts of climate change through increased severity of natural disasters and events. We are using integrative data science to help agencies respond to these impacts with limited resources through risk, resilience and expenditure profiling.
We are researching highly creative, low cost ways to transform and optimize the practice of emergency management and operations that bring together maker culture, data science, cybersecurity and human computer interaction.
Data Science in Disasters and Emergency Response is a short workshop-format introduction to using data science in emergency and disaster response that is written for first responders and emergency managers.
What if we could bring all the knowledge, data, insight, and prior decision-making of drug discovery together and use it to accelerate the discovery of new drugs? What if we could encode the millions of known relationships between potential new (or old) drugs, protein targets, genomics, biological processes, and disease mechanisms, and then use all this together to get new insights into disease and treatments?
A new report from FEMA shows efforts to encourage preparedness have failed, yet they are needed now more than ever. How can data science help?
There are now a plethora of apps and services that mean that if you do have internet service, you can to an amazing degree replicate the functions of an Emergency Operations Center (EOC) on the smartphone in your pocket. In this article we will describe a set of apps that will enable you to achieve the primary functions of the EOC: meeting & sharing space, phone and radio communications, situational awareness, and access to plans, documents and maps.
DS590 Data Science in Practice is a graduate course designed to help students gain critical, practical skills in applying data science to real world problems. Students will work in teams of 3-5 to tackle a real-world problem defined by a project sponsor. Project sponsors can be academics or industry practitioners. Students work with the project sponsor to understand the problem domain, identify where their data science skills can be applied, and to design, implement and test a solution.
In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges.
We have created a single repository called Chem2Bio2RDF by aggregating data from multiple chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions.
This is a five-minute flash talk on transforming pharmaceutical and healthcare companies into data companies.
INFO I426/516 Informatics in disasters and emergency response is an undergraduate/graduate elective designed for students interested in using technology and informatics skills in prevention of, mitigation of, response to and recovery from threats to safety, and emergency and disaster situations.
Info I590 Data and Society is a graduate course that introduces technically-trained students to the social, political, and ethical aspects of data science work. It is designed to create reflective practitioners who are able to think critically about how collecting, aggregating, and analyzing data are social processes, and processes that affect people.
I571 Introducing Cheminformatics: Navigating the world of chemical data is a gradate class that covers basic techniques of managing and analyzing chemical data on computers
Introducing Cheminformatics: an intensive self-study guide is a complete self-study guide designed to give you a rapid introduction to the emerging field of cheminformatics,
This is a talk given at the Pervasive Technology Institute at Indiana University on Big Data in Drug Discovery
Jaron Lanier’s Who Owns The Future is a must-read for anyone working with technology or data in the 21st century (i.e. all of us).