Skylar Connor
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
ORISE Post Doctoral Fellow
Also affiliated: United States Food and Drug Administration (2022–2025)
Postdoc Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Skylar Connor's research focuses on the application of artificial intelligence (AI) in regulatory science, particularly for evaluating drug safety and toxicity. Connor has investigated the adaptability of AI for drug-induced liver injury (DILI) assessment and developed resources to facilitate AI-driven toxicity studies. This includes work on generating lists of drugs associated with renal injury to support new approach methodologies for nephrotoxicity evaluation and creating a database (DILIrank 2.0) for drug-induced liver injury risk based on FDA labeling and literature reviews. Connor also explores the readiness of AI tools like ChatGPT for specific research applications in organ-specific drug toxicity. Their work emphasizes the need for best practices and reproducible science to advance AI in real-world applications, with a specific interest in drug labeling and its role in AI-based extraction of safety information. Connor's scholarship includes 8 publications and has garnered 76 citations, with an h-index of 5. They have collaborated with Ting Li, Weida Tong, Leihong Wu, and Minjun Chen at the National Center for Toxicological Research.
Metrics
- h-index: 5
- Publications: 8
- Citations: 85
Selected Publications
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DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025)
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Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research? (2025)
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Drug-induced kidney injury: challenges and opportunities (2024)
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Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024)
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Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury (2022)
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Best practice and reproducible science are required to advance artificial intelligence in real-world applications (2022)
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DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction (2021)
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Three Complete Genome Sequences of Genotype G Mumps Virus from the 2016 Outbreak in Arkansas, USA (2017)
Collaboration Network
Top Collaborators
- 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
- Drug-induced kidney injury: challenges and opportunities
- 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
Showing 5 of 6 shared publications
- 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
- Drug-induced kidney injury: challenges and opportunities
- 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
Showing 5 of 6 shared publications
- 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
- 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
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction
- 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
- Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury
- Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research?
- Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research?
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
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