| Distinct mechanical properties in homologous spectrin-like repeats of utrophin S Rajaganapathy, JL McCourt, S Ghosal, A Lindsay, PM McCourt, ... Scientific reports 9 (1), 5210, 2019 | 22 | 2019 |
| Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease K Bhattarai, S Rajaganapathy, T Das, Y Kim, Y Chen, ... Journal of the American Medical Informatics Association 30 (10), 1645-1656, 2023 | 16 | 2023 |
| Phosphorylation alters the mechanical stiffness of a model fragment of the dystrophin homologue utrophin MP Ramirez, S Rajaganapathy, AR Hagerty, C Hua, GC Baxter, J Vavra, ... Journal of Biological Chemistry 299 (2), 2023 | 10 | 2023 |
| Synoptic reporting by summarizing cancer pathology reports using large language models S Rajaganapathy, S Chowdhury, X Li, V Buchner, Z He, R Zhang, X Jiang, ... npj Health Systems 2 (1), 11, 2025 | 9 | 2025 |
| Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction S Chowdhury, Y Chen, P Li, S Rajaganapathy, A Wen, X Ma, Q Dai, Y Yu, ... Journal of the American Medical Informatics Association 31 (8), 1671-1681, 2024 | 6 | 2024 |
| Advancing efficacy prediction for electronic health records based emulated trials in repurposing heart failure therapies N Zong, S Chowdhury, S Zhou, S Rajaganapathy, Y Yu, L Wang, Q Dai, ... npj Digital Medicine 8 (1), 306, 2025 | 4 | 2025 |
| Artificial intelligence-based efficacy prediction of phase 3 clinical trial for repurposing heart failure therapies. N Zong, S Chowdhury, S Zhou, S Rajaganapathy, Y Yu, L Wang, Q Dai, ... Medrxiv: the Preprint Server for Health Sciences, 2023 | 4 | 2023 |
| Change detection using an iterative algorithm with guarantees S Rajaganapathy, J Melbourne, MV Salapaka Automatica 136, 110075, 2022 | 4 | 2022 |
| Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction T Das, K Bhattarai, S Rajaganapathy, L Wang, JR Cerhan, N Zong medRxiv, 2023 | 3 | 2023 |
| Learning and estimation of single molecule behavior S Rajaganapathy, J Melbourne, T Aggarwal, R Shrivastava, MV Salapaka 2018 Annual American Control Conference (ACC), 5125-5130, 2018 | 3 | 2018 |
| SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction S Chowdhury, S Rajaganapathy, L Sun, L Wang, P Yang, JR Cerhan, ... bioRxiv, 2025 | 2 | 2025 |
| Launching Insights: A Pilot Study on Leveraging Real-World Observational Data from the Mayo Clinic Platform to Advance Clinical Research Y Yu, X Hu, S Rajaganapathy, J Feng, A Abdelhameed, X Li, J Li, K Liu, ... arXiv preprint arXiv:2504.16090, 2025 | 1 | 2025 |
| Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction S Chowdhury, S Rajaganapathy, L Sun, J Cerhan, N Zong arXiv preprint arXiv:2309.10016, 2023 | 1 | 2023 |
| A Semi-Analytical Model to Investigate Cargo Transport by Bi-Directional Molecular Motor Ensemble R Shrivastava, S Bhaban, J Melbourne, S Rajaganapathy, M Salapaka Bulletin of the American Physical Society 64, 2019 | 1 | 2019 |
| Transport Properties of Molecular Motor Ensemble with Bi-Directiona I Motors: A Computational Approach. R Shrivastava, S Bhaban, S Rajaganapathy, M Li, TS Hays, MV Salapaka MOLECULAR BIOLOGY OF THE CELL 29 (26), 109-110, 2018 | 1 | 2018 |
| A Physics-Augmented Deep Learning Framework for Classifying Single Molecule Force Spectroscopy Data C Hua, S Rajaganapathy, RA Slick, J Vavra, JM Muretta, JM Ervasti, ... Forty-second International Conference on Machine Learning, 0 | 1 | |
| Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models MH Lee, S Chowdhury, X Li, S Rajaganapathy, EW Klee, P Yang, T Sio, ... arXiv preprint arXiv:2512.20633, 2025 | | 2025 |
| Emulating Clinical Trials with the Mayo Clinic Platform: Cardiovascular Research Perspective X Li, S Rajaganapathy, X Hu, J Feng, J Li, Y Yu, P Fiero, S Boroumand, ... medRxiv, 2025 | | 2025 |
| From ‘Negative’Trial to Positive Clinical Impact: Emulating WARCEF While Accounting for Selection Bias in Trial Timing X Li, S Rajaganapathy, X Hu, J Feng, J Li, Y Yu, P Fiero, S Boroumand, ... | | 2025 |
| Matching Patients to Clinical Trials using LLaMA 2 Embeddings and Siamese Neural Network S Chowdhury, S Rajaganapathy, Y Yu, C Tao, M Vassilaki, N Zong medRxiv, 2024 | | 2024 |
Nansu ZongDepartment of Artificial Intelligence and Informatics Research, Mayo ClinicVerified email at mayo.edu
