Use cases show Translator’s potential to expedite clinical research

RENCI investigators are contributing to the development of a platform called Biomedical Data Translator that will allow researchers to easily access and interrelate large amounts of data relevant to advancing biomedical research. Funded by the NIH’s National Center for Advancing Translational Sciences (NCATS), the new system is poised to accelerate translational clinical research by allowing users to approach biomedical questions from a holistic perspective to inspire important new research directions.

The platform is being developed by a 15-team multi-institutional Biomedical Data Translator consortium. Three of these teams include leadership from RENCI investigators. Although still a work in progress, Translator is being designed as an easy-to-use tool that can quickly respond to queries by identifying and synthesizing relevant data from a wide variety of sources.

Finding potential therapies for drug-induced liver injury

In December 2021, consortium members presented use cases to NCATS to demonstrate the platform’s progress and potential. In one, Paul Watkins, MD, from the UNC School of Medicine worked with RENCI collaborator Karamarie Fecho to use Translator to identify drugs that might be repurposed for treating drug-induced liver injury (DILI). There is a critical need for new therapies to heal liver damage caused by medicines. Although the injury sometimes heals when a patient stops taking the medication, it can take months or years to resolve and can leave patients unable to take medicines they need to treat medical conditions.

“There are lab-based ways to identify drugs for repurposing, or a researcher can spend years going through the literature and attempt to synthesize it,” explained Fecho. “Translator offers an alternative method that’s fast and doesn’t require the user to be an expert.” 

Using gene information to identify drug candidates that might hold promise for treating drug-induced liver injury, Translator quickly identified two antioxidant drugs for consideration. This query relied on clinical data that is part of UNC Health’s Integrated Clinical and Environmental Exposures Service (ICEES), which provides open, regulatory-compliant access to clinical data that is integrated with environmental exposures data. Fecho and colleagues from RENCI and the North Carolina Translational and Clinical Sciences Institute previously developed tools that allow Translator to access this important source of clinical data.

In addition to identifying potential drug candidates, Translator also provided experimental evidence that these drugs had been studied for preventing drug-induced liver injury in rat models and were used in clinical trials to treat other diseases. “Having this information showed that the candidate drugs were safe and effective enough to be used in a clinical trial,” said Fecho. “This can help reduce the risk involved in moving forward with clinical trials, which are time-consuming and expensive.”

The Translator findings are now being compiled into a formal report to present to the NIH-funded U.S. DILI Network leadership to inform planning for future clinical trials.

Revealing new directions for rare diseases

In another use case, researchers from the Hugh Kaul Precision Medicine Institute at the University of Alabama, Birmingham, are using Translator to find potential new treatments for rare diseases. Rare diseases are usually caused by gene mutations that aren’t passed on.

“For applications involving rare diseases, a new drug development candidate is not that helpful because it would require too much investment to develop and test a new drug for just a few people,” said RENCI’s Chris Bizon, co-PI of the Translator standards and reference implementation team. “Translator can help by looking for drugs that are already approved for some other purpose and have the potential to be repurposed for off-label use or tested in a clinical trial.”

The researchers were interested in a gene known as RHOBTB2. Children born with overactive variants of this gene sometimes never learn to walk and have severe intellectual disabilities. Researchers used Translator to ask for a list of all the chemicals that down-regulate RHOBTB2. When this didn’t return many leads, they performed another query to look for chemicals that up-regulate a gene that down-regulates RHOBTB2. This process helped reveal intermediate genes that could be targeted to down-regulate RHOBTB2.

“As a clinician, I don’t even know about all the databases that hold critical pieces of the puzzle I’m trying to put together,” said Anne Thessen, a visiting associate professor the University of Colorado School of Medicine. “With Translator I can prepare a query, run the query, and have results to review in an hour.”

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Biomedical Translator Platform moves to the next phase

New streamlined statistical method provides improved pattern detection and risk prediction for disease

The novel regression algorithm, CALF, outperforms the current gold standard, LASSO, in statistical tests

Researchers from the Renaissance Computing Institute (RENCI) at UNC-Chapel Hill, Perspectrix, the UNC School of Medicine, and the WVU Rockefeller Neuroscience Institute have collaborated to develop a new method for finding patterns in data which verifiably surpasses the performance of a generally accepted “gold standard.” 

Attempting to find patterns in data is central to all research, and it is particularly important in medical use of biological samples to predict a patient’s risk for disease formation and progression. Today, researchers can utilize advanced technology to produce an ocean of data about one person from various biological samples such as blood, DNA, and saliva, with the goal of identifying particular markers that can be informative about a person’s current health and future outlook. However, this advanced data collection and processing has outpaced current statistical methods for identifying simple but robust patterns and relationships, and this is particularly true for the field of psychiatry. For instance, researchers have yet to fully understand and predict the progression of schizophrenia. 

This new method, CALF, which stands for “coarse approximation linear function,” is described in the Scientific Reports paper, “A greedy regression algorithm with coarse weights offers novel advantages,” published on March 31, 2022. Application of CALF to five quite different examples from psychiatric and neurological studies consistently outperformed the gold standard, LASSO, or “least absolute shrinkage and selection operator” regression, and other methods. 

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New data format aids large-scale evolutionary biology research

In addition to revealing the hidden histories of life on Earth, studying the evolutionary relationships between organisms can help scientists track emerging diseases, inform methods to control invasive species, and understand how to best protect at-risk ecosystems.  

DNA sequencing and other genetic analysis approaches are providing vast new data streams to enable this research at unprecedented scales. For example, the Open Tree of Life Project is attempting to create a synthesized view of the evolutionary relationships among every known organism – more than 1.7 million species.

To aid in these endeavors, Gaurav Vaidya, PhD, from RENCI collaborated with a multi-institutional team of researchers to create a new data format that makes the clade definitions used by evolutionary biologists readable and interpretable by computers. Clades, which capture an organism’s ancestor and all its descendants, make up a portion of a phylogeny, a set of evolutionary relationships between different organisms.

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Biomedical Data Translator Platform moves to the next phase

Although we now have huge amounts of data on everything from genes to the causes of disease, it is stored in an enormous variety of ways and in many different locations. This makes it difficult, if not impossible, to find and use this data to think about biomedical questions in a big picture, holistic way.

The NIH’s National Center for Advancing Translational Sciences (NCATS) Biomedical Data Translator program is working to change this by funding a platform that allows scientists to easily access and interrelate data to inform new research directions. RENCI investigators are part of the leadership for three of the 15 teams that make up the Biomedical Data Translator consortium.

The Translator platform is designed to accelerate the development of new treatments and translational clinical research. For example, it could help uncover potential new therapies and drug targets, further elucidate how environmental exposures impact disease, and reveal new relationships between rare and common diseases.

“Translator offers a way of looking at a large amount of information – the equivalent to reading all the research papers ever published – and returning a reasonable amount of information,” said RENCI’s Chris Bizon, co-PI of the Translator standards and reference implementation team. “It provides a hypothesis that can be investigated and a list of information that will be helpful to this investigation.”

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Drone projects take data processing and communication to new heights

Communicating after a natural disaster is often critical but can be challenging if telecommunications lines are damaged or wireless networks become overwhelmed. Drones, however, can be used to quickly create an on-demand communication infrastructure that is not only useful for emergency situations but can also be used for transportation, surveillance and crop monitoring. 

RENCI researchers are contributing to cutting-edge research projects that aim to make drones even more useful by improving how their data is handled and by providing a testbed that helps researchers optimize drone-based communication. 

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RENCI researchers awarded 2021 Best Paper from the Elsevier FGCS Journal

RENCI researchers recently received the 2021 Best Paper Award from the Elsevier Future Generation Computer Systems (FGCS) Journal. The paper, titled “End-to-end online performance data capture and analysis for scientific workflows,” was co-authored by Cong Wang, Anirban Mandal, and collaborators from the DOE Panorama and RAMSES projects.

The FGCS Journal aims to lead the way in advances in distributed systems, collaborative environments, high performance computing (HPC), and big data on such infrastructures as grids, clouds, and the Internet of Things. Each year, the editorial board awards “Best Paper” to one submission featured in the journal.

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RENCI Internship Program: Investing in the Next Generation of Leaders

As part of RENCI’s mission to be a leader in data science, our team is dedicated to helping the next generation of thinkers bring their ideas to the table, build valuable skill sets, and pursue professional growth. While we’ve hosted interns in several areas of our work in the past, we have recently launched an Internship Program to provide organization-wide support and resources. We are excited to expand our reach and engage with curious and hard-working young professionals across RENCI’s research groups, collaborations, and operations teams. 

“Working as an intern at RENCI has been a meaningful experience to me,” said Yifei Wang, Atlantic Wave-SDX research assistant and intern. “Colleagues and supervisors were super patient and helpful while helping me to grow from a student to a professional. RENCI is the perfect place if you want to pursue your academic and career goals.” 

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NRIG Director Ilya Baldin inducted into the NC State Computer Science Alumni Hall of Fame

On October 10, 2021, Ilya Baldin was inducted into the North Carolina State University Computer Science Alumni Hall of Fame. This honor is granted to those alumni who have exhibited noteworthy contributions to their profession and the communities they serve. 

Throughout the course of his career, Baldin has led many projects in the computer science realm and has addressed major problems in data software development. From developing prototypes to creating technologies for testbeds, he has invested much of his time to make contributions to this field.

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ExoGENI: A critical step forward for edge cloud computing

After more than eight years in operation, the ExoGENI testbed is in the process of being decommissioned. RENCI researchers played leading roles in building, maintaining, and expanding the testbed, which provided a full-scale cloud system that thousands of researchers have used to test and deploy cutting-edge applications.

ExoGENI is one of two components that made up the NSF-funded Global Environment for Network Innovations (GENI) virtual laboratory project. Growing out of the need for a system to allow reproducible research involving computer science systems, distributed systems, and protocols, GENI was established to provide an open infrastructure for at-scale networking and distributed systems research and education across the U.S.

The ExoGENI testbed helped to pioneer edge cloud computing. This type of distributed computing uses many small computing installations rather than large, centralized computing resources located in a few places. Edge cloud computing speeds up data processing because computation and data storage is performed closer to the sources of data. Today, this computing approach is used for applications ranging from 5G mobile phone networks to autonomous driving.

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RENCI’s Network Research and Infrastructure Group works to advance the nation’s cyberinfrastructure

For more than ten years, the Network Research and Infrastructure Group (NRIG) at RENCI has been developing specialized cyberinfrastructure critical for advancing computer science and a variety of scientific domains. Their projects are helping scientists use large amounts of data to make new discoveries and have enabled important new advances in distributing computing networks, cloud-based systems, and software-defined networks.

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