| DeepMAsED: evaluating the quality of metagenomic assemblies O Mineeva, M Rojas-Carulla, RE Ley, B Schölkopf, ND Youngblut Bioinformatics 36 (10), 3011-3017, 2020 | 36 | 2020 |
| Don’t throw it away! the utility of unlabeled data in fair decision making M Rateike, A Majumdar, O Mineeva, KP Gummadi, I Valera Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 11 | 2022 |
| (1+ epsilon)-class classification: an anomaly detection method for highly imbalanced or incomplete data sets M Borisyak, A Ryzhikov, A Ustyuzhanin, D Derkach, F Ratnikov, ... Journal of Machine Learning Research 21 (72), 1-22, 2020 | 10 | 2020 |
| ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning O Mineeva, D Danciu, B Schölkopf, RE Ley, G Rätsch, ND Youngblut PLoS Computational Biology 19 (5), e1011001, 2023 | 4 | 2023 |
| Deep learning for inferring cause of data anomalies V Azzolini, M Borisyak, G Cerminara, D Derkach, G Franzoni, F De Guio, ... Journal of Physics: Conference Series 1085 (4), 042015, 2018 | 2 | 2018 |
| Tailoring Risk Prediction Models to Local Populations AN Zinzuwadia, O Mineeva, C Li, Z Farukhi, F Giulianini, B Cade, L Chen, ... JAMA cardiology 9 (11), 1018-1028, 2024 | 1 | 2024 |
| FAMEWS: a Fairness Auditing tool for Medical Early-Warning Systems M Hoche, O Mineeva, M Burger, A Blasimme, G Rätsch medRxiv, 2024.02. 08.24302458, 2024 | 1 | 2024 |
| Development and validation of a novel 10-year residual risk score (RRS) in the UK Biobank and Massachusetts general Brigham populations for patients with established ASCVD O Mineeva, C Li, F Giulianini, S Häfliger, V Bubes, G Raetsch, S Mora, ... Atherosclerosis 395, 2024 | | 2024 |
| Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK … O Lehmann, O Mineeva, D Veshchezerova, HJ Häuselmann, L Guyer, ... Journal of bone and mineral research 39 (8), 1103-1112, 2024 | | 2024 |
| Defining a Minimal Benchmark for Cardiovascular Risk Prediction Calculators in New England Electronic Health Record–Derived Cohort AN Zinzuwadia, O Mineeva, C Li, Z Farukhi, F Giulianini, BE Cade, ... Circulation: Cardiovascular Quality and Outcomes 17 (6), e010439, 2024 | | 2024 |
| Abstract P341: A Flexible, Interpretable Approach to Recalibrate Risk Prediction Models: Adapting the Pooled Cohorts Equation to a Local New England Contemporary Population AN Zinzuwadia, O Mineeva, C Li, L Chen, B Cade, E Karlson, Z Farukhi, ... Circulation 147 (Suppl_1), AP341-AP341, 2023 | | 2023 |
