M. Chandra
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Also affiliated: United States Food and Drug Administration (2025); Mangalore University (2014–2022); IPB University (2025); Purdue University West Lafayette (1993); Juniata College (2023)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
M. Chandra's research has focused on the study of spontaneous neoplasms and non-neoplastic lesions in laboratory animals, including Sprague-Dawley and Fischer 344 rats, as well as CD-1 and B6C3F1 mice. This work has contributed to the understanding of naturally occurring conditions in these models. Chandra has also explored the bioactivity, antioxidant, and antibacterial properties of medicinal plants, specifically Sauropus androgynus L. and Erythrina variegata L. Additionally, recent work has investigated walnut consumption and its effects on gut microbial metabolism through a randomized, crossover, controlled-feeding study. Chandra's scholarship metrics include an h-index of 8, with 23 total publications and 165 total citations. Collaborations include one shared publication with Ting Li from the National Center for Toxicological Research.
Metrics
- h-index: 8
- Publications: 22
- Citations: 165
Selected Publications
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GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use (2026)
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GanCtrl: Synthetic Control Predictions for Liver and Kidney Clinical-Pathology Profiles (Open TG-GATEs) (2025)
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GanCtrl: Synthetic Control Predictions for Liver and Kidney Clinical-Pathology Profiles (Open TG-GATEs) (2025)
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AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data (2025)
Collaboration Network
Top Collaborators
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data
- GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data
- GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use
- GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use
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