| The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires M Pavlović, L Scheffer, K Motwani, C Kanduri, R Kompova, N Vazov, ... Nature Machine Intelligence 3 (11), 936-944, 2021 | 59 | 2021 |
| Cross-reactive public TCR sequences undergo positive selection in the human thymic repertoire M Khosravi-Maharlooei, A Obradovic, A Misra, K Motwani, M Holzl, ... The Journal of clinical investigation 129 (6), 2446-2462, 2019 | 49 | 2019 |
| Human regulatory T cells from umbilical cord blood display increased repertoire diversity and lineage stability relative to adult peripheral blood K Motwani, LD Peters, WH Vliegen, AG El-Sayed, HR Seay, MC Lopez, ... Frontiers in immunology 11, 611, 2020 | 32 | 2020 |
| The future of blood testing is the immunome RA Arnaout, ETL Prak, N Schwab, F Rubelt, ... Frontiers in Immunology 12, 626793, 2021 | 31 | 2021 |
| Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification C Kanduri, M Pavlović, L Scheffer, K Motwani, M Chernigovskaya, V Greiff, ... GigaScience 11, giac046, 2022 | 23 | 2022 |
| Characterization of proinsulin T cell epitopes restricted by type 1 diabetes–associated HLA class II molecules EL Ihantola, H Ilmonen, A Kailaanmäki, M Rytkönen-Nissinen, A Azam, ... The Journal of Immunology 204 (9), 2349-2359, 2020 | 15 | 2020 |
| Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes PS Linsley, F Barahmand-pour-Whitman, E Balmas, HA DeBerg, KJ Flynn, ... JCI insight 6 (22), 2021 | 14 | 2021 |
| Human immune phenotyping reveals accelerated aging in type 1 diabetes MR Shapiro, X Dong, DJ Perry, JM McNichols, P Thirawatananond, ... JCI insight 8 (17), 2023 | 8 | 2023 |
| Generalized data thinning using sufficient statistics A Dharamshi, A Neufeld, K Motwani, LL Gao, D Witten, J Bien arXiv preprint arXiv:2303.12931, 2023 | 6 | 2023 |
| Superovulation in buffaloes; ovarian response to different regimes of FSH-P MC Yadav, AK Mishra, KT Motwani, S Rajeswaran, BV Joshi Proc. 2nd World Buffalo Congr. I 129, 1988 | 5 | 1988 |
| Binned multinomial logistic regression for integrative cell-type annotation K Motwani, R Bacher, AJ Molstad The Annals of Applied Statistics 17 (4), 3426-3449, 2023 | 2 | 2023 |
| Valid inference after prediction K Motwani, D Witten arXiv preprint arXiv:2306.13746, 2023 | 2 | 2023 |
| Multiresolution categorical regression for interpretable cell‐type annotation AJ Molstad, K Motwani Biometrics 79 (4), 3485-3496, 2023 | | 2023 |
| Revisiting inference after prediction K Motwani, D Witten Journal of Machine Learning Research 24 (394), 1-18, 2023 | | 2023 |
| Role of embryo transfer technology in buffalo improvement. KT Motwani | | 1987 |
| Supporting Information for “Multiresolution categorical regression for interpretable cell type annotation” AJ Molstad, K Motwani | | |
| Supplementary Material for “Binned multinomial logistic regression for integrative cell type annotation” K Motwani, R Bacher, AJ Molstad | | |
Todd BruskoProfessor, University of Florida, Department of Pathology; Diabetes Institute - Research DirectorVerified email at ufl.edu
