GIST Proposes New Direction for AI-Integrated Research on Early Diagnosis of Brain Diseases

InnoCORE Research Group Announces Its First Achievement

(From left) Dr. Junho Hwang and Professor Eunji Lee of the GIST Innocore Research Group, and Professor Nathan C. Gianneschi of Northwestern University, USA.

The Gwangju Institute of Science and Technology (GIST) announced on August 12 that Professor Eunji Lee's research team from the Department of Materials Science and Engineering at the GIST-InnoCORE Research Group, in collaboration with researchers from Northwestern University in the United States, has published research results that suggest a new direction for integrated research on "real-time protein imaging + AI analysis" for the early diagnosis of degenerative brain diseases such as Alzheimer's disease.

The GIST-InnoCORE Research Group is one of eight research teams participating in the national research talent development program "InnoCORE Project," promoted by the Ministry of Science and ICT, and is led by GIST.

The InnoCORE Project supports each of Korea's four major Institutes of Science and Technology (KAIST, GIST, DGIST, and UNIST) in operating a core research group in the field of AI convergence. The program recruits outstanding postdoctoral researchers, provides "multi-mentoring" from two or more mentors, and facilitates global collaborative research.

The GIST research group has set "AI + nanotechnology convergence for early diagnosis of brain diseases" as its core objective. This achievement marks the first case in which a research direction for integrated research was established from the early stages through the GIST-InnoCORE Research Group's "multi-mentoring system." After joining the GIST-InnoCORE Research Group as an "InnoCORE Fellow," Dr. Junho Hwang developed his research ideas under the guidance of Professor Eunji Lee, the lead mentor, and Professor Nathan C. Gianneschi of Northwestern University, the international expert mentor.

One of the main causes of degenerative brain diseases is the abnormal aggregation of proteins. The research team focused on the "two-step growth mechanism" reported by Professor De Yoreo's group at the University of Washington in the United States, which describes how silk proteins transition from an unstable amorphous cluster stage to a stable crystalline structure. Understanding this process allows for the control of the "growth direction" of proteins, which can be widely applied in future bio-diagnostic technologies and new materials design.

In this paper, the research team emphasized the importance of moving beyond conventional static protein analysis by observing protein structural changes in real time using in-situ transmission electron microscopy (TEM) and integrating these observations with machine learning-based AI analysis.

Professor Eunji Lee stated, "This research marks an important starting point for building the 'AI platform for early diagnosis of brain diseases and drug design' that the InnoCORE Research Group aims to develop. By applying AI technology to real-time transmission electron microscopy images, we can analyze the aggregation of proteins that cause brain diseases with greater precision at each stage of lesion development. Furthermore, by merging these results with existing clinical sample analysis data, this approach can be effectively utilized to predict the onset or progression of degenerative brain diseases such as Alzheimer's and Parkinson's disease."

Dr. Junho Hwang said, "Thanks to the mentors' support, I was able to gain a broad understanding of the scientific background and AI application potential of the research, and to develop new ideas for integrated research."

This research, supervised by Professor Eunji Lee of the GIST Department of Materials Science and Engineering and conducted by Dr. Junho Hwang (first author), was carried out in collaboration with Professor Nathan C. Gianneschi of Northwestern University. It was supported by the Ministry of Science and ICT's GIST-InnoCORE Project and the National Research Foundation of Korea's Mid-career Researcher Program.

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