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Principal Investigator: Daniel Burkhalter
Organization: RTUTOR, LLC
Fiscal Year: 2024
Award: $273,987
Funding agency: National Institute of General Medical Sciences
PROJECT SUMMARY
Large data sets are routinely collected in biomedical research. Analyzing such datasets often requires
coding, which can be challenging and time-consuming for researchers without computational expertise.
Capitalizing on recent breakthroughs in artificial intelligence (AI), we developed a prototype of RTutor.ai, a
website and a R package that enables users to "chat" with their data in dozens of natural languages. Under
a reactive programming environment, we use OpenAI's ChatGPT to translate users' requests into R or
Python code, which is then executed alongside user-uploaded data to produce results instantaneously. By
integrating prompt engineering, code generation, execution, and reporting, RTutor enables people without
coding experience to conduct preliminary analysis and visualization while boosting productivity for others by
providing candidate code for further refinement. Despite limited features, the current version has been used
by thousands of researchers world wild.
This project aims to fully develop the prototype and rigorously test and document its capacities and
limitations. Specific Aim 1: Develop and enhance RTutor. Based on our experience developing large
bioinformatics apps such as iDEP and ShinyGO, we will (1) optimize prompts for effective and robust code
generation, (2) upgrade to GPT-4 for better performance and improved code generation through
conversations, (3) handle multiple datasets, (4) improve user interface, (5) automate exploratory data
analysis, and (6) develop a customized version with built-in datasets and prompt structures for business
customers. Specific aim 2: Test, evaluate and document. (1) Unit and system testing, usability testing by
user groups with or without coding experiences, (2) systematic testing for limitations and robustness in
biomedical data analysis, (3) test the applicability in various bioinformatics tasks such as RNA-Seq data, (4)
document through text and video, (5) outreach to user communities.
The completion of this project will lead to the development of an AI-powered virtual assistant for statistical
computing. Like Amazon Alexa in our homes, its abilities might be limited but it can be made freely available
to all scientists across the world, partially mitigating the shortage of experienced statisticians and
bioinformaticians. RTutor has immense potential to accelerate scientific discovery and improve healthcare
outcomes by enabling more researchers, especially those with limited resources, to gain insights from large
datasets.
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