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Current · Arkansas
Last published
2026
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OpenAlex · ORCID
Refreshed
2026-08-06
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Biography and Research Information
OverviewAI-generated summary
Huixiao Hong's research centers on the application of machine learning and multi-omics data integration for toxicity prediction and cancer mutation detection. Hong has developed quantitative structure-activity relationship (QSAR) models to predict binding affinity, specifically focusing on PPARγ, using large datasets and machine learning algorithms. Their work also addresses the correction of batch effects in large-scale multiomics studies through reference-material-based ratio methods.
Further research includes developing machine learning models for predicting the cytotoxicity of nanomaterials and advancing best practices for cancer mutation detection using whole-genome and whole-exome sequencing. Hong is also involved in establishing community reference samples and data for benchmarking mutation detection methods. Their recent publications highlight a focus on epigenomics quality control and the integration of multi-omics data using quantitative profiling with reference materials.
Hong's scholarship metrics include an h-index of 73, 380 total publications, and over 20,000 citations, designating them as a highly cited researcher. Key collaborators at the National Center for Toxicological Research include Wenjing Guo, Tucker A. Patterson, Weida Tong, and Fan Dong, with whom Hong has co-authored numerous publications.
Metrics
- h-index: 73
- Publications: 380
- Citations: 20,717
Selected Publications
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Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders (2025)
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Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials (2025)
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AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women (2025)
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Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme (2025)
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Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals (2024)
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Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies (2024)
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Determining high priority disinfection byproducts based on experimental aquatic toxicity data and predictive models: Virtual screening and in vivo study (2024)
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Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study (2024)
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BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices (2024)
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Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials (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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Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project (2023)
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Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance (2023)
Collaboration Network
Top Collaborators
Tucker A. Patterson
National Center for Toxicological Research
16 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
Showing 5 of 16 shared publications
Wenjing Guo
National Center for Toxicological Research
15 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
Showing 5 of 15 shared publications
Jie Liu
National Center for Toxicological Research
14 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
Showing 5 of 14 shared publications
Luyao Ren
Fudan University (CN)
12 shared publications
- Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study
Showing 5 of 12 shared publications
Ying Yu
Fudan University (CN)
10 shared publications
- Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
- Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions
Showing 5 of 10 shared publications
Weida Tong
United States Food and Drug Administration
9 shared publications
In database
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
- The Quartet Data Portal: integration of community-wide resources for multiomics quality control
- Machine Learning Models for Predicting Liver Toxicity
Showing 5 of 9 shared publications
Jingcheng Yang
Fudan University (CN)
9 shared publications
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Assessing reproducibility of inherited variants detected with short-read whole genome sequencing
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
Showing 5 of 9 shared publications
Fan Dong
National Center for Toxicological Research
9 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
Showing 5 of 9 shared publications
Sugunadevi Sakkiah
National Center for Toxicological Research
7 shared publications
In database
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
Showing 5 of 7 shared publications
Andreas Scherer
University of Helsinki (FI)
7 shared publications
- Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- The SEQC2 epigenomics quality control (EpiQC) study
- Assessing reproducibility of inherited variants detected with short-read whole genome sequencing
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
- The Quartet Data Portal: integration of community-wide resources for multiomics quality control
Showing 5 of 7 shared publications
Yuanting Zheng
Fudan University (CN)
7 shared publications
- Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study
- Hidden biases in germline structural variant detection
Showing 5 of 7 shared publications
Jun Shang
Fudan University (CN)
7 shared publications
- Assessing reproducibility of inherited variants detected with short-read whole genome sequencing
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
- The Quartet Data Portal: integration of community-wide resources for multiomics quality control
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance
Showing 5 of 7 shared publications
Yaqing Liu
Fudan University (CN)
7 shared publications
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
- The Quartet Data Portal: integration of community-wide resources for multiomics quality control
Showing 5 of 7 shared publications
Naixin Zhang
Fudan University (CN)
7 shared publications
- Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
- The Quartet Data Portal: integration of community-wide resources for multiomics quality control
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance
Showing 5 of 7 shared publications
Zuowei Ji
United States Food and Drug Administration (US)
6 shared publications
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Machine Learning Models for Predicting Liver Toxicity
Showing 5 of 6 shared publications
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