Increasing efficiency and productivity of drug discovery powered by linked data graphs and AI / Machine Learning
Using integrative data science to mitigate natural disasters and climate change through risk, resilience and expenditure profiling
Improving outcomes in disasters and emergency response by smart integration of unconventional datasets, expertise and technologies
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.
David Wild is a practitioner and educator in informatics & computing, data science, and pharmaceutical research. He is associate professor in the School of Informatics, Computing and Engineering (SICE) and Director of the Integrative Data Science Lab at Indiana University, and is President of Data2Discovery, Inc.
This is a five-minute flash talk on transforming pharmaceutical and healthcare companies into data companies.
INFO I400/590 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).
How can you go about learning the skills needed to be a data scientist? Here are a variety of ways to learn a basic skill set.
What is data science, and what skills do I need to be a successful data scientist?