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, 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

Case Study: Applied Data Sciences Center (ADSC) Figured Prominently in Real-World Data Study of Type-2 Diabetes Published in Nature Medicine

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

Travis Johnson, PhD

Travis Johnson, PhD

Visiting Assistant Research Professor of Biostatistics & Health Data Sciences

Travis Johnson, PhD

Travis Johnson, PhD

Visiting Assistant Research Professor of Biostatistics & Health Data Sciences

Travis Johnson is an Assistant Research Professor of Biostatistics and Health Data Sciences at the Indiana University School of Medicine where he focuses on machine and deep learning, single-cell method development and domain adaptation.

Dr. Johnson received his bachelor's degree in plant biology from Ohio University. Next, he attended Community College of the Air Force where he obtained an associate's degree in scientific analysis technology. Then, he went on to earn his master's degree in public health and his doctorate in biomedical sciences from The Ohio State University.

Dr. Johnson joined the Indiana Biosciences Research Institute in July 2021 as a visiting assistant research professor to apply his academic experience in collaboration with institute scientists and external research partners to drive translational science that rapidly advances novel therapeutics for unmet medical needs.

Rong Qi

Rong Qi, PhD

Data Science Analyst, Applied Data Sciences Center

Rong Qi

Rong Qi, PhD

Data Science Analyst, Applied Data Sciences Center

Rong Qi received her BA in Biopharmacy at the Southwest Normal Univerity in China and then achieved her PhD in Biology under Dr. Xinyuan Liu at the Shanghai Jiaotong University. Following her PhD, she was the cell biology leader at Dragonfly Science. Next, Rong proceeded to St. Jude Children's Research Hospital for a postdoctoral assignment in the Tumor Cell Biology Department. Continuing to pursue her other passion, Rong went back to school at the Univeristy of Memphis to obtain a MS in Computer Science degree. At her internship in the Data Center Group at Intel, Rong was able to solve a challenging problem of integrating the MKL library into Cloudera and achieving the expected performance gain. Rong's mix of science and technology is a strong fit for the mission of IBRI.

Daniel Robertson

Daniel Robertson, PhD

Investigator and Director, Applied Data Sciences Center

Daniel Robertson

Daniel Robertson, PhD

Investigator and Director, Applied Data Sciences Center

Daniel H. Robertson, a proven and experienced technical leader in information technology (IT), computational science and research, is focused on defining and developing the Institute’s computational analytics, digital and data science capabilities.

Dr. Robertson originally joined the IBRI in mid-2015 as part of a loaned executive program at Eli Lilly and Company, but in mid-2017 he accepted a permanent position at the IBRI due to the opportunity at the IBRI to drive innovative research among multiple life sciences companies, academic institutions and technology companies to advance solutions to critical problems.

His most recent role at Eli Lilly and Company was Senior Director of Research IT where he led the IT team supporting discovery systems and processes across six global research sites and nine functional/therapeutic areas. During his leadership role in IT at Lilly, Dr. Robertson restructured Research IT to become a leaner, more efficient organization, reset the Research IT strategy supporting the discovery functions, delivered emerging new technology and analyses through informatics, enhanced support for HPC, cloud, and internal big data storage and analysis. He also transformed the IT support for Open Innovation Drug Discovery program to be the first high-performing DevOps team, which was recognized with an InformationWeek 500 award. Throughout his 10 years in leadership roles at Lilly, Dr. Robertson developed several individuals within his organization to advance to higher level roles in other organizations within Lilly. He joined Lilly as a research scientist in Lilly Research Laboratories and performed numerous independent contributor and scientific leadership roles before transitioning to the IT organization in 2010.

Dr. Robertson earned his PhD in physical chemistry from Florida State University and his Bachelor of Science degree in chemistry, graduating Summa Cum Laude, from Florida Southern College. After earning his PhD., Dr. Robertson served as an NRC/NRL Postdoctoral Research Associate at the Naval Research Laboratory in Washington, D.C., and then held several positions at Indiana University-Purdue University Indianapolis (IUPUI) from 1993 through 2000. He last served as Associate Scientist and Director of Technical and Administrative Services and Manager of the Facility for Computational Molecular Science at IUPUI before joining Eli Lilly and Company in 2000.

Dr. Robertson has published 67 papers in refereed journals, authored three invited book chapters, and conducted more than 65 professional/technical presentations. He has been honored with multiple awards from Eli Lilly and Company and IUPUI, and is a member of the American Chemical Society, American Physical Society and physics and mathematics honoraries.

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Applied Data Sciences Center

Focusing on the creation of tools and development of applications that will enable deep computer learning to assist researchers, clinicians, and patients.

Learn more


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