Yichan Li
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Post-doctoral Researcher
Also affiliated: Chinese Academy of Sciences (2020); Changjiang Water Resources Commission (2024); Northwest Institute of Eco-Environment and Resources (2020); Beijing University of Chemical Technology (2018); Lanzhou University (2019–2021)
Postdoc Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Yichan Li's research integrates machine learning with plant genetics, focusing on identifying genes associated with quantitative traits. A recent publication utilized an integrative machine learning approach for a soybean (Glycine max) yield case study, demonstrating the application of these computational methods in plant breeding. Li's work also extends to paleoclimatology and environmental science, with publications examining global drought-volatility, lake organic carbon sinks, and climate change connections between East Asia and North America since the Last Glacial Maximum. Additionally, research includes the linkage between granite weathering and gully erosion in subtropical regions. Li has authored 16 publications with 58 citations and an h-index of 5. Li collaborates with Linyin Cheng at the University of Arkansas at Fayetteville, with whom they share one publication.
Metrics
- h-index: 5
- Publications: 21
- Citations: 74
Selected Publications
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Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study (2026)
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Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study (2025)
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Observational Uncertainty for Global Drought‐Pluvial Volatility (2023)
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Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts (2021)
Collaboration Network
Top Collaborators
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study
- Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts
- Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts
- Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts
- Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts
- Changes of lake organic carbon sinks from closed basins since the Last Glacial Maximum and quantitative evaluation of human impacts
- Observational Uncertainty for Global Drought‐Pluvial Volatility
- Observational Uncertainty for Global Drought‐Pluvial Volatility
- Observational Uncertainty for Global Drought‐Pluvial Volatility
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative Machine Learning Approach for Identifying Genes Associated with Quantitative Traits: A Soybean Yield Case Study
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean ( <i>Glycine max</i> ) yield case study
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