Claire Zhong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Claire Zhong has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Chaoying Zhong
Department of Electrical Engineering and Automation, Guangdong Ocean University, Guangdong, China
1 shared publication - Jianli Lin
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
1 shared publication Junjie Hang
The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Sha Tin, China
1 shared publication- Junjie Huang
The Jockey Club School of Public Health and Primary Care, Faculty of Medicine the Chinese University of Hong Kong Hong Kong SAR China
1 shared publication - Lin Zhang
The School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
1 shared publication - Suwen Shi
Department of Chemistry, Boston University, Boston, USA
1 shared publication
Latest Publications
- Open Access
- 31 December 2024
- Published by Springer Nature in Chemistry Africa
- Vol. 8 (2) , 505-522
From 2015 to 2023: How Machine Learning Aids Natural Product Analysis
In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.
- 31 December 2024
- Published by Springer Nature in Chemistry Africa
- Vol. 8 (2) , 505-522
Open AccessFrom 2015 to 2023: How Machine Learning Aids Natural Product Analysis
In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.