| Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance AS Chowdhury, SM Reehl, K Kehn-Hall, B Bishop, BJM Webb-Robertson Scientific reports 10 (1), 19260, 2020 | 52 | 2020 |
| Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data G Clair, S Reehl, KG Stratton, ME Monroe, MM Tfaily, C Ansong, JE Kyle Bioinformatics 35 (21), 4507-4508, 2019 | 45 | 2019 |
| Rapid and flexible segmentation of electron microscopy data using few-shot machine learning S Akers, E Kautz, A Trevino-Gavito, M Olszta, BE Matthews, L Wang, Y Du, ... npj Computational Materials 7 (1), 187, 2021 | 44 | 2021 |
| Controlling the spatio-temporal dose distribution during STEM imaging by subsampled acquisition: In-situ observations of kinetic processes in liquids BL Mehdi, A Stevens, L Kovarik, N Jiang, H Mehta, A Liyu, S Reehl, ... Applied Physics Letters 115 (6), 2019 | 33 | 2019 |
| Predictive modeling of type 1 diabetes stages using disparate data sources BI Frohnert, BJ Webb-Robertson, LM Bramer, SM Reehl, K Waugh, ... Diabetes 69 (2), 238-248, 2020 | 27 | 2020 |
| An automated scanning transmission electron microscope guided by sparse data analytics M Olszta, D Hopkins, KR Fiedler, M Oostrom, S Akers, SR Spurgeon Microscopy and Microanalysis 28 (5), 1611-1621, 2022 | 22 | 2022 |
| Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers BJM Webb‐Robertson, LM Bramer, BA Stanfill, SM Reehl, ES Nakayasu, ... Journal of diabetes 13 (2), 143-153, 2021 | 22 | 2021 |
| Design of a graphical user interface for few-shot machine learning classification of electron microscopy data C Doty, S Gallagher, W Cui, W Chen, S Bhushan, M Oostrom, S Akers, ... Computational Materials Science 203, 111121, 2022 | 20 | 2022 |
| Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity YQ Susannah M. BURROWS, Aritra DASGUPTA, Sarah REEHL, Lisa BRAMER, Po-Lun MA ... Advances in Atmospheric Sciences 35 (9), 1101, 2018 | 13 | 2018 |
| Bayesian model averaging for ensemble-based estimates of solvation-free energies LJ Gosink, CC Overall, SM Reehl, PD Whitney, DL Mobley, NA Baker The Journal of Physical Chemistry B 121 (15), 3458-3472, 2017 | 12 | 2017 |
| Integration of infant metabolite, genetic, and islet autoimmunity signatures to predict type 1 diabetes by age 6 years BJM Webb-Robertson, ES Nakayasu, BI Frohnert, LM Bramer, SM Akers, ... The Journal of Clinical Endocrinology & Metabolism 107 (8), 2329-2338, 2022 | 11 | 2022 |
| The role of Nanocartography in the Development of Automated TEM M Olszta, K Fiedler, S Spurgeon, S Reehl, D Hopkins Microscopy and Microanalysis 27 (S1), 2986-2987, 2021 | 10 | 2021 |
| Extending classification algorithms to case-control studies B Stanfill, S Reehl, L Bramer, ES Nakayasu, SS Rich, TO Metz, M Rewers, ... Biomedical engineering and computational biology 10, 1179597219858954, 2019 | 9 | 2019 |
| Forecasting of in situ electron energy loss spectroscopy NR Lewis, Y Jin, X Tang, V Shah, C Doty, BE Matthews, S Akers, ... npj Computational Materials 8 (1), 252, 2022 | 7 | 2022 |
| Sensing analytical instrument parameters, specimen characteristics, or both from sparse datasets BA Stanfill, SM Reehl, MC Johnson, LM Bramer, ND Browning, ... US Patent 10,541,109, 2020 | 4 | 2020 |
| ROFI-the use of Repeated Optimization for Feature Interpretation BJM Webb-Robertson, LM Bramer, SM Reehl, TO Metz, Q Zhang, ... 2016 International Conference on Computational Science and Computational …, 2016 | 4 | 2016 |
| Implementing sub-sampling methods for low-dose (scanning) transmission electron microscopy (S/TEM) ND Browning, A Stevens, L Kovarik, A Liyu, BL Mehdi, B Stanfill, S Reehl, ... Microscopy and Microanalysis 23 (S1), 82-83, 2017 | 3 | 2017 |
| Machine learning for automated experimentation in scanning transmission electron microscopy SV Kalinin, D Mukherjee, K Roccapriore, BJ Blaiszik, A Ghosh, ... npj Computational Materials 9 (1), 227, 2023 | 2 | 2023 |
| Deep learning for automated experimentation in scanning transmission electron microscopy SV Kalinin, D Mukherjee, KM Roccapriore, B Blaiszik, A Ghosh, ... arXiv preprint arXiv:2304.02048, 2023 | 2 | 2023 |
| Pivot Point: The Key to TEM Automation M Olszta, K Fiedler, D Hopkins, K Yano, C Doty, M Oostrom, S Akers, ... Microscopy and Microanalysis 28 (S1), 2920-2921, 2022 | 2 | 2022 |
