Ting Li Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Postdoctoral
National Center for Toxicological Research
postdoc
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Biography and Research Information
OverviewAI-generated summary
Ting Li's research focuses on the application of advanced computational methods, particularly deep learning, to predict and understand complex biological and health-related phenomena. Li has investigated the potential of deep learning for predicting carcinogenicity, as demonstrated in the "DeepCarc" study, and for predicting drug-induced cardiotoxicity and Ames test outcomes, as seen in the "DICTrank" and "DeepAmes" publications, respectively. These efforts highlight a focus on developing predictive models with potential regulatory applications.
Further research by Li explores the intricate relationships between biological systems and health conditions. This includes investigating disruptions in brain connectivity in patients with Alzheimer's disease and mild cognitive impairment using resting-state functional MRI, and examining the diversity of intestinal microbiota in patients with alcohol use disorder and its correlation with alcohol consumption and cognition. Additionally, Li has contributed to systematic reviews and meta-analyses, such as the evaluation of hearing aid effects on cognitive functions in older adults, indicating a broad interest in synthesizing existing research to inform health outcomes.
Metrics
- h-index: 21
- Publications: 105
- Citations: 1,827
Selected Publications
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction (2025) DOI
- AIVIVE: a novel AI framework for enhanced <i>in vitro</i> to <i>in vivo</i> extrapolation (IVIVE) of toxicogenomics data (2025) DOI
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods (2025) DOI
- Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach (2024) DOI
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024) DOI
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling (2023) DOI
- Predicting drug-induced liver injury with artificial intelligence—a minireview (2023) DOI
- DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application (2023) DOI
- TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes (2023) DOI
- TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles Between Organs, Ages, and Sexes (2022) DOI
- Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation (2022) DOI
- Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury (2022) DOI
- Best practice and reproducible science are required to advance artificial intelligence in real-world applications (2022) DOI
- DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation (2021) DOI
- Correlations between sleep disturbance and brain cortical morphometry in healthy children (2021) DOI
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