T
Ting Li Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Postdoctoral
postdoc
22 h-index
123 pubs
1,923 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Ting Li's research focuses on the application of advanced computational methods, particularly deep learning, to address complex problems in toxicology, pharmacology, and neuroscience. Li has developed predictive models for drug-induced cardiotoxicity and mutagenicity, contributing to the assessment of drug safety and potential regulatory applications. These efforts include the creation of comprehensive databases, such as DICTrank, which ranks human drugs by their risk of cardiotoxicity based on FDA labeling.
Further research explores the relationship between biological factors and health outcomes. This includes investigating the impact of the gut microbiota on cognitive function in individuals with alcohol use disorder and examining the effects of hearing aids on cognitive abilities in older adults. Li's work also extends to neuroimaging, analyzing functional connectivity in the brain to understand conditions like Alzheimer's disease and intermittent exotropia.
With a scholarly record including 105 publications and 1,867 citations, and an h-index of 22, Li is recognized as a highly cited researcher. Key collaborations include numerous shared publications with Weida Tong and Skylar Connor at the National Center for Toxicological Research. Li currently leads a research group and has remained active in research, with the most recent publication in 2025.
Metrics
- h-index: 22
- Publications: 123
- Citations: 1,923
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 <i>in vitro</i> to <i>in vivo</i> 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
Weida Tong
United States Food and Drug Administration (US)
15 shared publications
- 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
- 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
Showing 5 of 15 shared publications
Ruth Roberts
University of Birmingham (GB)
9 shared publications
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
- 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
- TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes
Showing 5 of 9 shared publications
Shraddha Thakkar
United States Food and Drug Administration (US)
7 shared publications
- 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
- Best practice and reproducible science are required to advance artificial intelligence in real-world applications
- Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation
Showing 5 of 7 shared publications
Skylar Connor
United States Food and Drug Administration
4 shared publications
In database
- 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
Zhichao Liu
National Center for Toxicological Research (US)
4 shared publications
- 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
Yanyan Qu
University of Arkansas at Little Rock (US)
4 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
Tianyi Yan
Beijing Institute of Technology (CN)
3 shared publications
- Episodic memory in aspects of brain information transfer by resting-state network topology
- Modular-level alterations of single-subject gray matter networks in schizophrenia
- Episodic memory in aspects of brain information transfer by resting-state network topology
Bin Wang
Taiyuan University of Technology (CN)
3 shared publications
- Episodic memory in aspects of brain information transfer by resting-state network topology
- Modular-level alterations of single-subject gray matter networks in schizophrenia
- Episodic memory in aspects of brain information transfer by resting-state network topology
Xiaoxia Qu
Beijing Tongren Hospital (CN)
3 shared publications
- Decreased Functional Connectivity of the Primary Visual Cortex and the Correlation With Clinical Features in Patients With Intermittent Exotropia
- Altered stability of dynamic brain functional architecture in primary open-angle glaucoma: a surface-based resting-state fMRI study
- Intrinsic network changes associated with cognitive impairment in patients with hearing loss and tinnitus: a resting-state functional magnetic resonance imaging study
Zhichao Liu
Integra (United States) (US)
3 shared publications
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Best practice and reproducible science are required to advance artificial intelligence in real-world applications
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
Dongying Li
National Center for Toxicological Research
3 shared publications
In database
- 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
Yanping Mao
University of Chinese Academy of Sciences (CN)
2 shared publications
- Disrupted balance of long and short-range functional connectivity density in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study
- Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study
Zhengluan Liao
Zhejiang Provincial People's Hospital (CN)
2 shared publications
- Disrupted balance of long and short-range functional connectivity density in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study
- Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study
Jiaojiao Hu
University of Chinese Academy of Sciences (CN)
2 shared publications
- Disrupted balance of long and short-range functional connectivity density in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study
- Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study
Dansheng Le
Zhejiang Chinese Medical University (CN)
2 shared publications
- Disrupted balance of long and short-range functional connectivity density in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study
- Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study
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