Biography and Research Information
OverviewAI-generated summary
Kristin Ashby's research focuses on understanding the factors that contribute to drug-induced liver injury (DILI). Her work investigates how drug properties and host characteristics influence the biochemical presentation and recovery timeline of DILI. Ashby has explored the use of machine learning approaches to predict DILI risk, including identifying the interaction of single-nucleotide polymorphisms as a potential risk factor for chronic DILI. Her publications also examine the role of drug transporters and metabolizing enzymes in DILI. Collaborating with colleagues at the National Center for Toxicological Research, Ashby has contributed to computational modeling efforts aimed at predicting hepatotoxicity from drugs and chemicals. Her scholarship includes 9 publications with 180 citations and an h-index of 6.
Metrics
- h-index: 6
- Publications: 9
- Citations: 181
Selected Publications
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Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals (2023)
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Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury (2021)
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Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs (2021)
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Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI (2021)
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Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach (2021)
Collaboration Network
Top Collaborators
Minjun Chen
United States Food and Drug Administration
5 shared publications
In database
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
- Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs
A. González-Jiménez
Instituto de Investigación Biomédica de Málaga (ES)
2 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
Ayako Suzuki
Durham VA Medical Center (US)
2 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
Ismael Álvarez‐Álvarez
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
2 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
Raúl J. Andrade
Instituto de Investigación Biomédica de Málaga (ES)
2 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
M. Isabel Lucena
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
2 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
Tsung‐Jen Liao
United States Food and Drug Administration
2 shared publications
In database
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Wei Zhuang
United States Food and Drug Administration
1 shared publication
In database
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
Guruprasad P. Aithal
University of Nottingham (GB)
1 shared publication
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
Yue Wu
National Center for Toxicological Research (US)
1 shared publication
- Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs
R. E. Moore
National Center for Toxicological Research
1 shared publication
In database
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
Jie Liu
United States Food and Drug Administration
1 shared publication
In database
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Yue Wu
National Center for Toxicological Research (US)
1 shared publication
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Leihong Wu
United States Food and Drug Administration
1 shared publication
In database
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Weida Tong
National Center for Toxicological Research (US)
1 shared publication
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
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