우리 연구실은 바이오마커 농축, 고감도 검출, 그리고 AI 기반 바이오마커 분석에 이르는 분자진단의 전 과정을 아우르고 있습니다. 미충족 임상 수요를 바탕으로, 복잡한 생체 신호를 정밀하고 임상적으로 의미 있는 정보로 변환하는 통합 진단 플랫폼을 개발하고 있습니다. 또한 의학, 생명과학, 화학, 재료과학, 공학, 데이터과학 등 다양한 분야와의 융합 연구를 통해 다양한 질환에 적용 가능한 차세대 진단 기술을 구현하고자 합니다.

We cover the full spectrum of molecular diagnostics, from biomarker enrichment and high-sensitivity detection to AI-driven biomarker analysis. Driven by unmet clinical needs, we develop integrated diagnostic platforms that convert complex biological signals into precise and clinically meaningful information. Through interdisciplinary collaboration across medicine, life sciences, chemistry, materials science, engineering, and data science, we aim to advance next-generation diagnostic technologies for a wide range of diseases.

• We develop advanced sample preparation technologies that enable efficient enrichment, isolation, and preservation of low-abundance biomarkers from complex clinical specimens.

• Our research focuses on microfluidic systems, functional nanomaterials, and chemically engineered interfaces to capture and enrich diverse molecular biomarkers, including pathogens, cells, nucleic acids, and extracellular vesicles, from a wide range of biofluids, such as blood, urine, and oral swabs.

• By improving the earliest step of the diagnostic workflow, we enhance sensitivity, minimize sample loss, and establish a reliable foundation for downstream molecular analysis.

• We develop high-sensitivity biosensing and molecular detection platforms that enable reliable and precise detection of molecular targets.

• Our research focuses on integrating diverse sensing strategies, including CRISPR-based molecular recognition, optical and electrochemical detection, and microfluidic systems, together with isothermal amplification approaches to effectively process and detect low-abundance biomarkers.

• By advancing detection performance and system integration, we enable rapid, accurate, and robust molecular diagnostics across a wide range of clinical applications.

• We develop AI-driven analytical frameworks to identify optimal biomarker combinations and maximize diagnostic performance from complex molecular datasets.

• Our research focuses on integrating biomarker profiling with machine learning, deep learning, and algorithmic approaches for molecular target analysis and design to improve sensitivity, specificity, and accuracy in disease detection and classification.

• By transforming molecular data into clinically actionable insights, we aim to establish robust and data-driven diagnostic strategies applicable to a wide range of diseases, including cancer and infectious diseases.