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Comparing Eastern and Western Gut Microbiota in Parkinson’s Disease Using Machine Learning

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 10th International Conference on Next Generation Computing 2024 (2024.11)바로가기
  • 페이지
    pp.369-371
  • 저자
    Hamin Im, Yedam Park, Dongjin Oh, Chan-Uk Yeom, Keun-Chang Kwak
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A468887

원문정보

초록

영어
While the gut microbiota is increasingly implicated in the pathogenesis of Parkinson's Disease (PD), the majority of existing research predominantly focuses on Western populations, with limited studies addressing Eastern cohorts. This study aims to elucidate the differential composition of gut microbiota between Eastern and Western PD patients by utilizing advanced machine learning techniques. 16S ribosomal RiboNucleic Acid (rRNA) sequencing data from stool samples are obtained from the Sequence Read Archive (SRA), comprising a random selection of 70 individuals (35 healthy controls and 35 PD patients) from four countries: Korea, Japan, the United States, and Italy. Recursive Feature Elimination (RFE) is employed for feature selection, and four machine learning models— Support Vector Machine (SVM), Random Forest (RF), k- Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost)—are applied to classify PD patients by geographic origin. RFE identifies 15 key microbial taxa that distinguish between healthy controls and PD patients. Among the models trained on these taxa, the RF model exhibits the highest predictive accuracy, achieving 0.83 ± 0.048. Despite the relatively small sample size, this study underscores the necessity for larger-scale investigations and contributes to a more comprehensive understanding of gut microbiota disparities between Eastern and Western populations in the context of PD.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHOD
III. RESULTS
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCE

키워드

Parkinson’s Disease Gut Microbiota Geographic variability 16srRNA Machine learning

저자

  • Hamin Im [ dept. of Biomedical Science Chosun University Gwangju, Republic of Korea ]
  • Yedam Park [ dept. of Biomedical Science Chosun University Gwangju, Republic of Korea ]
  • Dongjin Oh [ dept. of Biomedical Science Chosun University Gwangju, Republic of Korea ]
  • Chan-Uk Yeom [ Division of AI Convergence College Chosun University Gwangju, Republic of Korea ] Corresponding Author
  • Keun-Chang Kwak [ dept. of Electronics Engineering Chosun University Gwangju, Republic of Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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