On May 15th, CCADD members gathered for a Teacher’s Day dinner and expressed their appreciation to Prof. Howard. The event brought together CCADD members, graduate students, and student researchers in a warm and collegial setting. Despite the rainy weather, CCADD alumni—Dr. Siun Kim, Dr. Yoona Choi, and JungHyun Won—added a special significance to the evening and the lasting bonds formed within the lab. 

During the dinner, attendees shared updates on their research, academic experiences, and personal reflections. The most memorable moment of the evening was when the graduate students and CCADD members presented Prof. Howard with handwritten letters expressing appreciation and respect for Prof. Howard’s guidance. The messages conveyed sincere appreciation for Prof. Howard’s continued support. CCADD members’ letter included a quote that captured the spirit of Teacher's Day: “A good teacher is like a candle—it consumes itself to light the way for others.” Prof. Howard expressed heartfelt thanks.

The event provided an opportunity to welcome new members and recognize the ongoing contributions of current CCADD researchers. In addition, the event served as a meaningful moment to reconnect with CCADD alumni. Above all, the gathering was a chance to once again express sincere gratitude for Prof. Howard’s enduring mentorship and support. The evening was both meaningful and enjoyable for all who attended.

One of our team’s studies has been recently accepted for publication in Scientific Reports, a peer-reviewed open-access journal from the Nature Portfolio. The study, entitled "Improving ACS Prediction in T2DM Patients by Addressing False Records in Electronic Medical Records Using Propensity Score," proposed a novel method to tackle inaccuracies in electronic medical records (EMRs)—specifically, false positives, false negatives (including potentially missing data) in clinical information that is expressed as binary categorical variables.

The study redefined propensity scores to adjust for incorrect or incomplete data entries. Our team demonstrated that this correction approach enhanced both the predictive performance and interpretability of machine learning models assessing the risk of acute coronary syndrome (ACS) in patients undergoing basal insulin therapy.

This research not only explored the application of statistical tools in digital health but also underscored the potential of machine learning to deliver more reliable and actionable predictions in routine clinical care.

This study was supported by the Seokcheon Nanum Foundation’s Future Medicine Research Support Program.

On March 10, the Yongeon Lab of CCADD completed its renovation, marking a new phase in the research infrastructure. Equipped with high-performance computers and upgraded research facilities, the Yongeon Lab now offers a more efficient and productive environment for ongoing projects. The renovation was made possible through the generous support of Prof. Howard Lee and the collaborative efforts of team members. The enhanced space is expected to significantly boost research capacity.

Alongside the renovation, CCADD has launched the full-scale operation of its dual-site research system, connecting the Yongeon Lab with the Gwanggyo Lab. The two labs are tightly integrated, ensuring smooth collaboration and optimal resource sharing. With this dual-site model, CCADD aims to build a more flexible and interconnected research ecosystem—reinforcing its position as a leading hub for their research.

Suhyun An is currently an undergraduate researcher at CCADD after completing the 2024 winter internship program. She is majoring in  Stem Cell and Regenerative Biotechnology at Konkuk University . Her recent interests lie in research utilizing large language models and deep neural networks, with a particular focus on AI-based drug discovery. More specifically, she focuses on the applications of drug-drug interactions and PK/PD modeling. Her research goal is to leverage these technologies to predict drug toxicity using clinical notes, as well as explore machine learning models for drug repurposing.

Yura Choi is currently an undergraduate researcher at CCADD after successfully completing the 2024 Winter Internship Program. She is majoring in Chemistry at Kyung Hee University. During her internship, she participated in developing a deep learning-based Drug-Drug Interaction (DDI) prediction model, utilizing protein information to enhance model performance. Through this project, she gained hands-on experience in data processing, programming, and predictive modeling. She continues to explore various approaches to DDI prediction modeling and has a growing interest in ADMET prediction and PK/PD modeling. She aims to apply AI-powered data analysis to contribute to novel drug development strategies and innovations. 

Seoyoon Jang is currently an undergraduate researcher at CCADD after diligently completing the 2024 winter internship program. Seoyoon Jang is majoring in Systems Biotechnology at Chung-Ang University. Her research interests include generating real-world evidence (RWE) based on real-world data (RWD) using electronic medical records (EMR) and regulatory science. During her internship, she became proficient in R and gained hands-on experience with both representation and generative LLM models.

Nayoung Ku is currently an undergraduate researcher at CCADD, following the successful completion of her internship program. She is pursuing studies in life science and AI convergence at Handong Global University, with a previous Bachelor's degree in psychology. Nayoung specializes in developing prediction modeling and optimization algorithms with diverse datasets.

Her research includes work at the Biodata Laboratory developing cancer diagnostic machine learning models, and she received the Daewoong Pharmaceutical CEO Award at the JUMP AI 2024 Drug Discovery Competition for her team's work on IRAK4 inhibitor prediction.

Seeun Park is currently an undergraduate researcher at CCADD. She became an undergraduate student researcher after successfully completing the 2024 Winter Internship program. She is a Bioengineering major at Yonsei University. She was able to gain knowledge and hands-on experience on artificial intelligence for clinical drug development by the project “Improving the Generalizability of Natural Language Process Models to Extract Drug Safety Information from Clinical Notes”. With passion in healthcare and safety, she focuses on exploring clinical pharmacology and regulatory science more specifically Real-World Data (RWD) and policy analysis in Korea. It is her goal to work on expertise for the improvement of disease prevention and control.