| Yoonjung Yoonie Joo / Ph.D. |
Name : Yoonjung Yoonie Joo Ph.D.
Department : SAIHST
Title : Assistant professor
Office : 일원역캠퍼스 TBA
E-mail : yoonjungjoo@skku.edu
Homepage : https://gennielab.weebly.com/
Lab. title : GENNIE [진이眞理] Lab, Genomics and Neural-Network Intelligence for HealthcarE
Related Department : Department of Digital health
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■ Education and Experience 2011 고려대학교 생명과학 학사 (부전공: 경영학) 2013 美 Northwestern University McCormick School of Engineering 노스웨스턴대학 공과대학 생명공학(Biotechnology) 석사 2020 美 Northwestern University Feinberg School of Medicine 노스웨스턴대학 의과대학 의생명정보학(Health and Biomedical Informatics) 박사 2020 서울대학교 사회과학연구원 박사후연구원 2021-2023 고려대학교 데이터과학원 연구교수 2021-2023 서울대학교 AI연구원 객원연구원 2023-현재 성균관대학교 SAIHST 디지털헬스학과 조교수 ■ 연구 분야 전공분야 의료/유전체/라이프로그 빅데이터 기반 건강과 질병의 예측, 예방 및 처방적분석 연구실 실험실명: GENNIE [진이眞理] Lab, Genomics and Neural-Network Intelligence for HealthcarE 홈페이지: https://gennielab.weebly.com/ ■ 소개 Accelerating Digital Health through Genomic Insights and Neural-Network Intelligence The mission of the GENNIE [진이眞理] Lab is to improve our understanding of the underlying genetic architecture of human complex traits ranging from human intelligence, cognitive ability, mental illness to clinical diseases by leveraging multidimensional biomedical data and emerging AI technologies. Integrating multiple types of omics data, including electronic health records (EHRs), brain neuroimaging data or social longitudinal survey data, with large-scale DNA data is a driving focus of the laboratory. ■ Research Interests ▷ Genomic-driven Precision Medicine (GDPM): Our lab's research mission is to advance precision medicine by integrating large-scale genomic and clinical data, including electronic health records (EHR)-linked biobank databases, to improve our understanding of the genetic basis of complex diseases and to develop more accurate predictive models for personalized treatment. ▷ Sociogenomics or Medical Social Sciences: Sociogenomics is an interactionist approach that emphasizes the dynamic relationship between genes and environment. It recognizes that genetic and environmental effects are constantly changing rather than being fixed, as believed by classical biometric approaches. Our interest lies in how genetics can serve as a reference point to understand external changes at both micro- and macro-scales, enabling us to interpret social change within the context of our inherited biological background. ▷ AI/ML approach in healthcare big data: We aim to develop AI/ML-based predictive models linking functional genomic and environmental perturbations to changes in phenotypes, which have the potential to revolutionize our understanding of disease mechanisms and improve patient outcomes. ▷ Salutogenesis: One of our goals is to advance our understanding of salutogenesis, the progression of an individual from being less healthy to a healthier state, and eventually create personalized treatments that consider an individual's genetic makeup as well as their health history and environmental surroundings. ▷ Imaging Genetics: In order to develop more accurate predictive models, it is imperative to integrate genetic, neuroimaging, and environmental data. Our research involves applying AI and machine learning algorithms to analyze multi-modal data, such as imaging (MRI), genetic (genotype), and diagnostic interview (K-SADS) data, to classify individuals’ state of mental health outcomes, such as cognitive ability, suicidal behaviors, depression, bipolar disorder, mood disorders, or ADHD. 본 연구실은 다가올 초고령사회의 정밀의학 실현을 위해, 최신 딥러닝/머신러닝 기술을 다차원 의생명데이터(유전체, 이미징, 사회환경 데이터 등)에 적용함으로서 건강상태의 맞춤형 예측, 예방, 조기진단 및 처방적 분석에 기여하는 것을 목표로 한다. 특히 다양한 형태의 최신 심층신경망 모델링을 의생명 빅데이터에 도입함으로서, 산재되어있던 단일 모달리티 정보를 효과적으로 통합하고, 한 개인에 대한 건강정보를 입체적으로 파악하여, 질병의 발병에 영향을 미치는 통합적인 위험요인들을 발굴하는 ‘커넥티드 헬스’(Connected Health)을 지향한다. 우리 연구실은 특히 (1) 코로나 팬데믹과 초고령화에 따른 현대사회 한국인들을 위한 연구 (2) 단편적인 데이터로 파악이 어려운 다인자성(multifactorial) 건강예측을 위한 멀티모달(multimodal) 데이터 기반 융합연구 (3) 인공지능 패러다임 변화를 견인중인 최신 머신러닝/딥러닝 신경망 기반 계산기술들의 의료/유전체/라이프로그 데이터 적용에 관심이 많으며, 이외에도 의과학적 발견을 임상적 지식으로 전환하여 국민의 건강을 증진하고 디지털헬스케어 서비스 개선에 기여할 수 있는 다양한 연구주제를 환영한다. 동시에 유럽인종 위주의 생명정보학적(bioinformatic) 접근법과 발견을 한국인 유전체-뇌이미징 데이터에 단계적으로 적용 및 결과비교를 위한 한국인-아시아인 바이오뱅크 구축에 참여중이다. ■ 최근 대표 연구업적(2019-현재) (최근 3년간의 주요연구실적 5편이내) 1. Joo YY, Moon SY, Wang HH, Kim H, Lee EJ, Kim JH, Posner J, Ahn WY, Choi I, Kim JW, Cha J. Association of genome-wide polygenic scores for multiple psychiatric and common traits in preadolescent youths at risk of suicide. JAMA network open. 2022;5(2):e2148585. doi:10.1001/jamanetworkopen.2021.48585 (Psy) (PRS) (ML) 2. Park J, Lee E, Cho G, Hwang H, Joo YY*, Cha J*. Gene-Environment Pathways to Cognitive Development and Psychotic-Like Experiences in Children. eLife. 2023. doi:10.7554/eLife.88117.1 (Neuro) (Psy) (PRS) 3. Joo YY, Cha J, Freese J, Hayes MG. Cognitive Capacity Genome-Wide Polygenic Scores Identify Individuals with Slower Cognitive Decline in Aging. Genes. 2022 Jul 24;13(8):1320. (Psy) (PRS) 4. Saunders GR, Wang X, Chen F, Jang SK, Liu M, Wang C, Gao S, Jiang Y, Khunsriraksakul C, Otto JM, Addison C, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature. 2022 Dec 7:1-7. (GWAS) 5. Joo YY, Kim J, Lee K, Cho GJ, Yi KW. Misperception of body weight and associated socioeconomic and health-related factors among Korean female adults: A nationwide population-based study. Frontiers in Endocrinology. 2022;13. (EHR) (ML) Imaging genetics, Psychiatric genetics, Polygenic Prediction, Electronic Health Records, Machine Learning(Deep learning) |
