| A path following algorithm for the graph matching problem M Zaslavskiy, F Bach, JP Vert IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12), 2227 …, 2008 | 431 | 2008 |
| Deep learning-based classification of mesothelioma improves prediction of patient outcome P Courtiol, C Maussion, M Moarii, E Pronier, S Pilcer, M Sefta, ... Nature medicine 25 (10), 1519-1525, 2019 | 270 | 2019 |
| Global alignment of protein–protein interaction networks by graph matching methods M Zaslavskiy, F Bach, JP Vert Bioinformatics 25 (12), i259-1267, 2009 | 225 | 2009 |
| Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ... Nature communications 10 (1), 2674, 2019 | 207 | 2019 |
| A deep learning model to predict RNA-Seq expression of tumours from whole slide images B Schmauch, A Romagnoni, E Pronier, C Saillard, P Maillé, J Calderaro, ... Nature communications 11 (1), 3877, 2020 | 180 | 2020 |
| Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides C Saillard, B Schmauch, O Laifa, M Moarii, S Toldo, M Zaslavskiy, ... Hepatology 72 (6), 2000-2013, 2020 | 136 | 2020 |
| A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction B Hoffmann, M Zaslavskiy, JP Vert, V Stoven BMC bioinformatics 11, 1-16, 2010 | 107 | 2010 |
| Chromosomal context and epigenetic mechanisms control the efficacy of genome editing by rare-cutting designer endonucleases F Daboussi, M Zaslavskiy, L Poirot, M Loperfido, A Gouble, V Guyot, ... Nucleic acids research 40 (13), 6367-6379, 2012 | 101 | 2012 |
| Phrase-based statistical machine translation as a traveling salesman problem M Zaslavskiy, M Dymetman, N Cancedda Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, 2009 | 31 | 2009 |
| Many-to-many graph matching: a continuous relaxation approach M Zaslavskiy, F Bach, JP Vert Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 30 | 2010 |
| A path following algorithm for graph matching M Zaslavskiy, F Bach, JP Vert Image and Signal Processing: 3rd International Conference, ICISP 2008 …, 2008 | 27 | 2008 |
| A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction MP Menden, D Wang, Y Guan, MJ Mason, B Szalai, KC Bulusu, T Yu, ... BioRxiv, 200451, 2017 | 25 | 2017 |
| Open community challenge reveals molecular network modules with key roles in diseases S Choobdar, ME Ahsen, J Crawford, M Tomasoni, T Fang, D Lamparter, ... bioRxiv, 265553, 2018 | 24 | 2018 |
| Phrase-based statistical machine translation as a generalized traveling salesman problem M Zaslavskiy, M Dymetman, N Cancedda US Patent 8,504,353, 2013 | 19 | 2013 |
| Community assessment of cancer drug combination screens identifies strategies for synergy prediction MP Menden, D Wang, Y Guan, M Mason, B Szalai, KC Bulusu, T Yu, ... bioRxiv 200451, 1-32, 2017 | 13 | 2017 |
| Transcriptomic learning for digital pathology B Schmauch, A Romagnoni, E Pronier, C Saillard, P Maillé, J Calderaro, ... BioRxiv, 760173, 2019 | 10 | 2019 |
| ToxicBlend: Virtual screening of toxic compounds with ensemble predictors M Zaslavskiy, S Jégou, EW Tramel, G Wainrib Computational Toxicology 10, 81-88, 2019 | 8 | 2019 |
| Efficient design of meganucleases using a machine learning approach M Zaslavskiy, C Bertonati, P Duchateau, A Duclert, GH Silva BMC bioinformatics 15 (1), 1-11, 2014 | 8 | 2014 |
| Graph matching and its application in computer vision and bioinformatics M Zaslavskiy PhD thesis, l’Ecole nationale superieure des mines de Paris, 2010 | 7 | 2010 |
| Exploring the transcription activator-like effectors scaffold versatility to expand the toolbox of designer nucleases A Juillerat, M Beurdeley, J Valton, S Thomas, G Dubois, M Zaslavskiy, ... BMC molecular biology 15 (1), 1-10, 2014 | 6 | 2014 |
