Biography and Research Information
OverviewAI-generated summary
Zoe Li's research program investigates the application of machine learning and deep learning techniques across various scientific domains. Li has published on the use of these methodologies for toxicity prediction, brain tumor MRI image segmentation, and facilitating machine learning in drug discovery through protein-ligand interaction fingerprinting. Additional work explores the effects of environmental factors on hydrological processes, including runoff, sediment yields, and vegetational cover changes in river basins under climate change. Li has also examined the dynamic resilience of hydropower infrastructure in multihazard environments and the operational safety of dam systems. Li's scholarship metrics include an h-index of 13, with 39 total publications and 478 total citations. Key collaborators at the National Center for Toxicological Research include Tucker A. Patterson, Fan Dong, Wenjing Guo, and Minjun Chen.
Metrics
- h-index: 13
- Publications: 38
- Citations: 499
Selected Publications
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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques (2025)
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Computational Toxicology (2024)
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Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development (2024)
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Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery (2024)
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Machine learning and deep learning for brain tumor MRI image segmentation (2023)
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Review of machine learning and deep learning models for toxicity prediction (2023)
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Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment (2023)
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QSAR models for predicting in vivo reproductive toxicity (2023)
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EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity (2023)
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Decision forest—a machine learning algorithm for QSAR modeling (2023)
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Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor (2023)
Collaboration Network
Top Collaborators
Tucker A. Patterson
United States Food and Drug Administration
11 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Showing 5 of 11 shared publications
Huixiao Hong
United States Food and Drug Administration
11 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Showing 5 of 11 shared publications
Fan Dong
United States Food and Drug Administration
10 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- QSAR models for predicting in vivo reproductive toxicity
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Showing 5 of 10 shared publications
Jie Liu
National Center for Toxicological Research
9 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- QSAR models for predicting in vivo reproductive toxicity
Showing 5 of 9 shared publications
Wenjing Guo
National Center for Toxicological Research
8 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- QSAR models for predicting in vivo reproductive toxicity
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Showing 5 of 8 shared publications
Ahmed Badr
Cairo University (EG)
3 shared publications
- Dynamic Resilience Quantification of Hydropower Infrastructure in Multihazard Environments
- Dam System and Reservoir Operational Safety: A Meta-Research
- Probabilistic dynamic resilience quantification for infrastructure systems in multi-hazard environments
Wael El‐Dakhakhni
McMaster University (CA)
3 shared publications
- Dynamic Resilience Quantification of Hydropower Infrastructure in Multihazard Environments
- Dam System and Reservoir Operational Safety: A Meta-Research
- Probabilistic dynamic resilience quantification for infrastructure systems in multi-hazard environments
Ruili Huang
National Center for Advancing Translational Sciences (US)
3 shared publications
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Menghang Xia
National Institutes of Health (US)
3 shared publications
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Md Kamrul Hasan Khan
National Center for Toxicological Research (US)
3 shared publications
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Zeyu Zhao
Tsinghua University (CN)
3 shared publications
- Unveiling the spatial heterogeneity of factors influencing physical and perceived recovery disparities under extreme rainstorms: A geographically weighted machine learning approach
- Post-disaster recovery planning for infrastructure systems based on residents’ needs: A hypernetwork approach
- The Risk of Misjudging Risks: Human Perceptions vs. Physical Recovery in City Resilience
Dongping Fang
Tsinghua University (CN)
3 shared publications
- Unveiling the spatial heterogeneity of factors influencing physical and perceived recovery disparities under extreme rainstorms: A geographically weighted machine learning approach
- Post-disaster recovery planning for infrastructure systems based on residents’ needs: A hypernetwork approach
- The Risk of Misjudging Risks: Human Perceptions vs. Physical Recovery in City Resilience
Yanpeng Cai
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN)
2 shared publications
- Effects of a cascade reservoir system on runoff and sediment yields in a River Basin of southwestern China
- Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China
Yulei Xie
Guangdong University of Technology (CN)
2 shared publications
- Effects of a cascade reservoir system on runoff and sediment yields in a River Basin of southwestern China
- Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China
Pengxiao Zhou
Northwestern University (US)
2 shared publications
- Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion
- Sustainable Water Resource Management: Challenges and Opportunities
Similar Researchers
Based on overlapping research topics
Fan Dong
National Center for Toxicological Research
Machine Learning Applications
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Daniel Irabor
University of Arkansas at Pine Bluff
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Minjun Chen
National Center for Toxicological Research
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Tucker A. Patterson
National Center for Toxicological Research
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Kamel Mansouri
National Center for Toxicological Research
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Wenjing Guo
National Center for Toxicological Research
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