| SELFIES and the future of molecular string representations M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ... Patterns 3 (10), 2022 | 87 | 2022 |
| Nanoporous and wrinkled electrodes enhance the sensitivity of glucose biosensors RC Adams-McGavin, Y Chan, CM Gabardo, J Yang, M Skreta, BC Fung, ... Electrochimica Acta 242, 1-9, 2017 | 32 | 2017 |
| Errors are useful prompts: Instruction guided task programming with verifier-assisted iterative prompting M Skreta, N Yoshikawa, S Arellano-Rubach, Z Ji, LB Kristensen, ... arXiv preprint arXiv:2303.14100, 2023 | 30 | 2023 |
| Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves JCC Kwong, A Khondker, JK Kim, M Chua, DT Keefe, J Dos Santos, ... Pediatric Nephrology, 1-8, 2021 | 25 | 2021 |
| Solution-processed wrinkled electrodes enable the development of stretchable electrochemical biosensors Y Chan, M Skreta, H McPhee, S Saha, R Deus, L Soleymani Analyst 144 (1), 172-179, 2019 | 25 | 2019 |
| Automatically disambiguating medical acronyms with ontology-aware deep learning M Skreta, A Arbabi, J Wang, E Drysdale, J Kelly, D Singh, M Brudno Nature communications 12 (1), 5319, 2021 | 20 | 2021 |
| Predicting obstructive hydronephrosis based on ultrasound alone L Erdman, M Skreta, M Rickard, C McLean, A Mezlini, DT Keefe, AS Blais, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 19 | 2020 |
| Predictive accuracy of prenatal ultrasound findings for lower urinary tract obstruction: A systematic review and Bayesian meta‐analysis DT Keefe, JK Kim, E Mackay, M Chua, T Van Mieghem, P Yadav, M Lolas, ... Prenatal Diagnosis 41 (9), 1039-1048, 2021 | 17 | 2021 |
| A machine learning-based approach for quantitative grading of vesicoureteral reflux from voiding cystourethrograms: Methods and proof of concept A Khondker, JCC Kwong, M Rickard, M Skreta, DT Keefe, AJ Lorenzo, ... Journal of Pediatric Urology 18 (1), 78. e1-78. e7, 2022 | 15 | 2022 |
| Training without training data: Improving the generalizability of automated medical abbreviation disambiguation M Skreta, A Arbabi, J Wang, M Brudno Machine Learning for Health Workshop, 233-245, 2020 | 15 | 2020 |
| Large language models for chemistry robotics N Yoshikawa, M Skreta, K Darvish, S Arellano-Rubach, Z Ji, ... Autonomous Robots 47 (8), 1057-1086, 2023 | 13 | 2023 |
| Pre‐versus postnatal presentation of posterior urethral valves: a multi‐institutional experience P Yadav, M Rickard, J Weaver, M Chua, JK Kim, A Khondker, K Milford, ... BJU international 130 (3), 350-356, 2022 | 13 | 2022 |
| The silent trial-the bridge between bench-to-bedside clinical AI applications JCC Kwong, L Erdman, A Khondker, M Skreta, A Goldenberg, ... Frontiers in digital health 4, 929508, 2022 | 11 | 2022 |
| Multi-institutional validation of improved vesicoureteral reflux assessment with simple and machine learning approaches A Khondker, JCC Kwong, P Yadav, JYH Chan, A Singh, M Skreta, ... The journal of urology 208 (6), 1314-1322, 2022 | 10 | 2022 |
| Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification M Skreta, A Luccioni, D Rolnick Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020, 2020 | 10 | 2020 |
| Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves JK Weaver, K Milford, M Rickard, J Logan, L Erdman, B Viteri, N D’Souza, ... Pediatric Nephrology 38 (3), 839-846, 2023 | 8 | 2023 |
| Phenopad: building AI enabled note-taking interfaces for patient encounters J Wang, J Yang, H Zhang, H Lu, M Skreta, M Husić, A Arbabi, N Sultanum, ... NPJ digital medicine 5 (1), 12, 2022 | 8 | 2022 |
| RePLan: Robotic replanning with perception and language models M Skreta, Z Zhou, JL Yuan, K Darvish, A Aspuru-Guzik, A Garg arXiv preprint arXiv:2401.04157, 2024 | 5 | 2024 |
| 3D photography based neural network craniosynostosis triaging system P Mashouri, M Skreta, J Phillips, D McAllister, M Roy, S Senkaiahliyan, ... Machine Learning for Health, 226-237, 2020 | 5 | 2020 |
| Reinforcement learning supercharges redox flow batteries Y Cao, CT Ser, M Skreta, K Jorner, N Kusanda, A Aspuru-Guzik Nature Machine Intelligence 4 (8), 667-668, 2022 | 4 | 2022 |
