Ting Li
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Postdoctoral
Also affiliated: University of North Carolina at Charlotte (2018); Shenyang Pharmaceutical University (2016–2024); Qingdao University (2021); Xi'an University of Science and Technology (2023); Jiangnan University (2025); Beijing Language and Culture University (2023); Beijing Institute of Technology (2020–2022); Beijing Tongren Hospital (2016–2023); United States Food and Drug Administration (2019–2026); Nanjing Tech University (2016); Southwest University (2022); Radboud University Nijmegen (2020); Shandong University (2012); Harbin Medical University (2022); Jinan University (2024); Xuzhou Medical College (2024); Beijing University of Chinese Medicine (2022); Second Military Medical University (2026); Beijing Technology and Business University (2024); Capital Medical University (2016–2023); South China Normal University (2022); Xiamen University (2023); Jilin University (2021); Chinese Academy of Sciences (2012); Sichuan University (2023); Beijing Normal University (2019); Chang'an University (2024); Inner Mongolia University (2023); Shanghai Mental Health Center (2011); Radboud University Medical Center (2020); Southwest Medical University (2025); China University of Geosciences (2024); Affiliated Hospital of Southwest Medical University (2025); West China Hospital of Sichuan University (2023); Dalian University (2017); Yan'an University (2023); Ezhou Central Hospital (2024); Third Affiliated Hospital of Harbin Medical University (2022); Shanghai Institute of Materia Medica (2012); Gansu Provincial Hospital (2016); Shenyang First People's Hospital (2023); Zhujiang Hospital (2021); Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine (2022); Peking University People's Hospital (2023); Second Affiliated Hospital of Soochow University (2016); Chengdu University (2022); National Center for Drug Screening (2012); Zhejiang Provincial People's Hospital (2021); Beijing Institute of Graphic Communication (2022); Renmin Hospital of Wuhan University (2025); Arkansas Children's Nutrition Center (2020); First Affiliated Hospital of Harbin Medical University (2022); Shanghai Changning Mental Health Center (2016–2024); First Affiliated Hospital Zhejiang University (2022); State Key Laboratory of Drug Research (2012); Donders Institute for Brain, Cognition and Behaviour (2020); Beijing Advanced Innovation Center for Food Nutrition and Human Health (2024); Huazhong University of Science and Technology (2024); Shaanxi University of Science and Technology (2023); Sichuan Normal University (2022–2025); Xi’an Jiaotong-Liverpool University (2012–2022); Zhejiang Gongshang University (2007–2011); Zhejiang University (2022); Beihang University (2018); Taiyuan University of Technology (2020); Guangzhou Medical University (2018)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ting Li's research focuses on neuroimaging and its application to understanding brain function and disorders, particularly in the context of drug-induced liver injury and neurodevelopmental conditions. Li has investigated the effects of cognitive training on resting-state functional connectivity within key brain networks, including the default mode, salience, and central executive networks. Further work has explored the neuroprotective mechanisms of Sirt1 against focal cerebral ischemia in rats, highlighting the involvement of the Nrf2/antioxidant defense pathway and the potential benefits of hyperbaric oxygen preconditioning.
Li's research also extends to large-scale neuroscience consortia, such as the ENIGMA project, which aims to advance the understanding of conditions like attention deficit/hyperactivity disorder and autism spectrum disorder through collaborative neuroimaging analysis. This work leverages advanced analytical techniques and large datasets to identify commonalities and differences in brain structure and function across diverse populations. Li has a substantial publication record, with 107 publications and over 2,058 citations, contributing to a high-impact researcher designation. Key collaborators include Weida Tong, Skylar Connor, and Dongying Li from the National Center for Toxicological Research, and Xiawei Ou from the University of Arkansas for Medical Sciences.
Metrics
- h-index: 24
- Publications: 103
- Citations: 2,048
Positions
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Postdoctoral 2021–presentNational Center for Toxicological Research Division of Bioinformatics and Biostatistics ORCID
Selected Publications
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Integrating in vitro and in silico NAMs for enhanced prediction of drug-induced liver injury (2026)
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Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction (2025)
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AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data (2025)
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Federated learning: a privacy-preserving approach to data-centric regulatory cooperation (2025)
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DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods (2025)
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Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach (2024)
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Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024)
Collaboration Network
Top Collaborators
- Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
- Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
Showing 5 of 19 shared publications
- Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
- DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation
- Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
Showing 5 of 12 shared publications
- Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
- DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation
- Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
Showing 5 of 10 shared publications
- Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
- Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury
- Predicting drug-induced liver injury with artificial intelligence—a minireview
Showing 5 of 6 shared publications
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- Best practice and reproducible science are required to advance artificial intelligence in real-world applications
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- Best practice and reproducible science are required to advance artificial intelligence in real-world applications
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Cortical Morphometry is Associated with Neuropsychological Function in Healthy 8‐Year‐Old Children
- Brain Cortical Structure and Executive Function in Children May Be Influenced by Parental Choices of Infant Diets
- Correlations between sleep disturbance and brain cortical morphometry in healthy children
- Cortical Morphometry is Associated with Neuropsychological Function in Healthy 8‐Year‐Old Children
- Brain Cortical Structure and Executive Function in Children May Be Influenced by Parental Choices of Infant Diets
- Correlations between sleep disturbance and brain cortical morphometry in healthy children
- Scoring Matrix for Unstandardized Data in Entity Resolution
- When Entity Resolution Meets Deep Learning, Is Similarity Measure Necessary?
- Scoring Matrix for Unstandardized Data in Entity Resolution
- When Entity Resolution Meets Deep Learning, Is Similarity Measure Necessary?
- Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles Between Organs, Ages, and Sexes
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