Research Summary

Today’s world is increasingly driven by data. Every day massive amounts of data are generated and captured, and the rate of data generation continues to accelerate. In the life sciences, this convergence of information technology and biology presents an exciting opportunity for future innovation.

To address the need to integrate biology and data in ways that produce meaningful results, the IBRI has developed unique capabilities focused on the application of data to solve big problems.

Led by Dr. Dan Robertson and Dr. Nitesh Chawla, the Applied Data Sciences Center focuses on the creation of tools and development of applications that will enable deep computer learning to assist researchers, clinicians, and patients.

Projects

Understanding of Public Health in Indiana

IBRI worked with several government and non-profit health organizations to drive a rapid collaborative project to explore the available information for improved understanding of public health in Indiana. This project provided insights into available regional data and facilitates planning on how to better access and integrate data in the future to drive evidence-based programs and measure success within the state. This project created new relationships between non-IBRI organizations and action is already being performed to increase the quality and coverage of this data. A follow-on collaboration is being planned to extend this project to provide improved insights at the county level in Indiana.

Digital Phenotype/Biomarker Data Collection

With the rapid emergence of digital devices and associated toolkits such as Apple’s ResearchKit for iOS, personal devices are expected to be a key factor in understanding non-biological factors as it relates to disease. Based on input from one of IBRI’s key stakeholders, IBRI initiated a collaboration with an entrepreneurial company that had prior experience in health-related platforms to explore how to best drive research and innovation with these devices. Discussions with clinician groups are ongoing to define new collaborations to use this innovative secure platform to generate data to assist clinical research, “Digital Biomarker,” or capture and improve patient outcomes, “Digital Phenotype.”

Better Understanding Type 2 Diabetes

As shown by other research, Type 2 diabetes (T2D) is a complex, multi-symptomatic metabolic disease with variability in the patient population related to disease progression, therapeutic response, or related complications. IBRI coordinated a project with Eli Lilly and Company and Roche Diagnostics to analyze clinical information available through the Regenstrief Institute to better understand patient subgroups, related disease characteristics, and complications of patients with T2D. In a secondary effort based on the findings of this project, IBRI will work with its partners to analyze biological samples to identify potential new therapeutic targets or biomarkers related to the disease subtypes. 

Molecular Safety Assessment

Timely decisions on the impact of molecules on human health and the environment are key to accelerating research by more quickly focusing efforts on molecules that are more likely to meet regulatory requirements and societal expectations. The IBRI joined Dow AgroSciences and Eli Lilly and Company in a collaboration to generate a shared data analysis platform for early assessment of molecules, potentially saving companies time and millions in development costs with a “fail fast” model. The goal of the project is to develop a collaborative platform to analyze the data from past toxicological assessments to assist more rapid and accurate assessment of new molecules. This platform will capitalize on best practices of both industrial organizations to more effectively and efficiently analyze data going forward. Ultimately, the informatics platform will be made publicly available to expand learning and accelerate discovery at academic institutions.

Lab Team


Highlights and Publications

  • DH Robertson, DW Brenner, JW Mintmire, "Energetics of nanoscale graphitic tubules", Physical Review B 45 (21) 1992, 12592
  • M Vieth, JJ Sutherland, DH Robertson, RM Campbell, “Kinomics: characterizing the therapeutically validated kinase space”, Drug discovery today 10 (12) 2005, 839-846
  • G Wu, DH Robertson, CL Brooks, M Vieth, “Detailed analysis of grid-based molecular docking: A case study of CDOCKER—A CHARMm-based MD docking algorithm”, Journal of computational chemistry 24 (13) 2003, 1549-1562
  • M Vieth, MG Siegel, RE Higgs, IA Watson, DH Robertson, KA Savin, ... “Characteristic physical properties and structural fragments of marketed oral drugs”, Journal of medicinal chemistry 47 (1) 2004, 224-232

View All of Daniel Robertson’s Publications

  • Nagrecha, S. and Chawla, N.V., 2016. Quantifying decision making for data science: from data acquisition to modeling. EPJ Data Science, 5(1), p.27.
  • Dasgupta, D. and Chawla, N.V., 2016, October. MedCare: Leveraging Medication Similarity for Disease Prediction. In Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference on (pp. 706-715). IEEE.
  • Zhang, Y., Zhang, L., Oki, E., Chawla, N.V. and Kos, A., 2016. IEEE Access Special Section Editorial: Big Data analytics for Smart and connected health. IEEE Access, 4, pp.9906-9909.
  • Feldman, K., Stiglic, G., Dasgupta, D., Kricheff, M., Obradovic, Z. and Chawla, N.V., 2016. Insights into Population Health Management Through Disease Diagnoses Networks. Scientific Reports, 6.

View All of Nitesh Chawla’s Publications