생명정보기술(BITL) 연구실

구성원 소개

지도교수: 오세종 SW융합대학 소프트웨어학과

연혁

2004

01월 01일
연구실 설립 (천안)

2017

01월 01일
죽전 캠퍼스로 이전

연구분야

  • 바이오 및 의료 분야에 필요한 빅데이터, 머신러닝 기술 연구
  • Data analysis
  • Explainable Artificial Intelligence (XAI)
  • Machine learning
  • Feature engineering
  • Classification/Prediction model

연구내용 및 보유기술

<빅데이터 기반 안과 진단 기술/ 대사증후군 진단 연구>
1. Development of the Integrated Glaucoma Risk Index (Diagnostics. 2022)
2. Explainable Machine Learning Model for Glaucoma Diagnosis
and Its Interpretation (Diagnostics. 2021)
3. Development of machine learning models for diagnosis of glaucoma (PloS one, 2017)
4. Robust Metabolic Syndrome Risk Index Based on Triangular Areal Similarity (IEEE BIBM, 2021)
5. A Preliminary Result of Food Object Detection using Swin Transformer (ICCTA, 2022)
6. Prediction of Metabolic Syndrome based on Non-invasive Measurement Features for Chronic Disease Management (ICCTA, 2022)
<Feature Engineering>
1. Hybrid-recursive feature elimination for efficient feature selection (Applied Sciences, 2020)
2. Feature Interaction in Terms of Prediction Performance (Applied Sciences, 2019)
3. Improved measures of redundancy and relevance for mRMR feature selection(Computers, 2019)
4. Clustering and R* Value-Based Evaluation Measures of Feature Subset for Health Informatics (Journal of Medical Imaging and Health Informatics, 2018)
5. Improving feature selection performance using pairwise pre-evaluation (BMC bioinformatics, 2016)
<설명 가능한 AI>
1. Predictive case-based feature importance and interaction (Information Sciences, 2022)
2. Analysis of Misclassified Cases in a Metabolic Syndrome Prediction Model: Explain misclassification of a prediction model (ICCTA, 2022)
<특허출원>
1. 머신 러닝 예측 모델의 피처 중요도 산출 방법 및 이를 수행하기 위한 컴퓨팅 장치( METHOD FOR CALCULATING FEATURE IMPORTANCE OF MACHINE
LEARNING PREDICTIVE MODEL AND COMPUTING DEVICE FOR
EXECUTING THE SAME). KR Patent No. 2-2005-016634-4 (Sep. 17. 2021)
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