| Predicting breast cancer by applying deep learning to linked health records and mammograms A Akselrod-Ballin, M Chorev, Y Shoshan, A Spiro, A Hazan, R Melamed, ... Radiology 292 (2), 331-342, 2019 | 37 | 2019 |
| Changing the approach to treatment choice in epilepsy using big data O Devinsky, C Dilley, M Ozery-Flato, R Aharonov, Y Goldschmidt, ... Epilepsy & Behavior 56, 32-37, 2016 | 31 | 2016 |
| Paradoxical hypersusceptibility of drug-resistant mycobacteriumtuberculosis to β-lactam antibiotics KA Cohen, T El-Hay, KL Wyres, O Weissbrod, V Munsamy, C Yanover, ... EBioMedicine 9, 170-179, 2016 | 21 | 2016 |
| Modular memoization, tracking and train-data management of feature extraction R Aharonov, Y Goldschmidt, M Ozery-Flato, C Yanover US Patent 10,572,822, 2020 | 18 | 2020 |
| Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study A Gottlieb, C Yanover, A Cahan, Y Goldschmidt BMJ Open Diabetes Research and Care 5 (1), 2017 | 11 | 2017 |
| Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins Z Waks, O Weissbrod, B Carmeli, R Norel, F Utro, Y Goldschmidt Scientific reports 6 (1), 1-12, 2016 | 7 | 2016 |
| Smarter log analysis E Aharoni, S Fine, Y Goldschmidt, O Lavi, O Margalit, M Rosen-Zvi, ... IBM Journal of Research and Development 55 (5), 10: 1-10: 10, 2011 | 7 | 2011 |
| Fast multilevel clustering Y Goldschmidt, M Galun, E Sharon, R Basri, A Brandt | 7 | 2005 |
| Adaptive methods for classification of biological microarray data from multiple experiments Y Goldschmidt, E Sharon, FJ Quintana, IR Cohen, A Brandt | 7 | 2003 |
| Integrated multisystem analysis in a mental health and criminal justice ecosystem E Falconer, T El-Hay, D Alevras, JP Docherty, C Yanover, A Kalton, ... Health & justice 5 (1), 1-8, 2017 | 6 | 2017 |
| Fast and efficient feature engineering for multi-cohort analysis of EHR data M Ozery-Flato, C Yanover, A Gottlieb, O Weissbrod, ... Stud Health Technol Inform 235, 181-5, 2017 | 5 | 2017 |
| Automatic detection of anomalies in graphs Y Goldschmidt, O Lavi, M Ninio US Patent 9,245,233, 2016 | 5 | 2016 |
| Automatic detection of anomalies in graphs Y Goldschmidt, O Lavi, M Ninio US Patent 9,245,233, 2016 | 5 | 2016 |
| Workflow validation and execution E Aharoni, Y Goldschmidt, T Lavee, H Neuvirth-Telem US Patent 8,601,481, 2013 | 5 | 2013 |
| A standard based approach for biomedical knowledge representation A Farkash, H Neuvirth, Y Goldschmidt, C Conti, F Rizzi, S Bianchi, E Salvi, ... Conference of the European Federation of Medical Informatics (MIE): August …, 2011 | 4 | 2011 |
| An evaluation toolkit to guide model selection and cohort definition in causal inference Y Shimoni, E Karavani, S Ravid, P Bak, TH Ng, SH Alford, D Meade, ... arXiv preprint arXiv:1906.00442, 2019 | 3 | 2019 |
| Automatic detection of anomalies in graphs Y Goldschmidt, O Lavi, M Ninio US Patent App. 14/839,981, 2016 | 3 | 2016 |
| Framework for identifying drug repurposing candidates from observational healthcare data M Ozery-Flato, Y Goldschmidt, O Shaham, S Ravid, C Yanover medRxiv, 2020 | 2 | 2020 |
| Characterizing Subpopulations with Better Response to Treatment Using Observational Data-an Epilepsy Case Study M Ozery-Flato, T El-Hay, R Aharonov, N Parush-Shear-Yashuv, ... bioRxiv, 290585, 2018 | 2 | 2018 |
| Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease D Laifenfeld, C Yanover, M Ozery-Flato, O Shaham, M Rosen-Zvi, N Lev, ... medRxiv, 2020 | 1 | 2020 |
