| Systematic quantitative analysis of H2A and H2B variants by targeted proteomics S El Kennani, A Adrait, O Permiakova, AM Hesse, C Ialy-Radio, M Ferro, ... Epigenetics & Chromatin 11, 1-18, 2018 | 24 | 2018 |
| Distinguishing between spectral clustering and cluster analysis of mass spectra H Borges, R Guibert, O Permiakova, T Burger Journal of proteome research 18 (1), 571-573, 2018 | 6 | 2018 |
| CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis O Permiakova, R Guibert, A Kraut, T Fortin, AM Hesse, T Burger BMC bioinformatics 22 (1), 1-30, 2021 | 5 | 2021 |
| Sketched Stochastic Dictionary Learning for large‐scale data and application to high‐throughput mass spectrometry O Permiakova, T Burger Statistical Analysis and Data Mining: The ASA Data Science Journal 15 (1), 43-56, 2022 | 3 | 2022 |
| Using unlabeled data to discover bivariate causality with deep restricted Boltzmann machines N Sokolovska, O Permiakova, SK Forslund, JD Zucker IEEE/ACM transactions on computational biology and bioinformatics 17 (1 …, 2018 | 3 | 2018 |
| A semi-supervised approach to discover bivariate causality in large biological data N Sokolovska, O Permiakova, SK Forslund, JD Zucker Machine Learning and Data Mining in Pattern Recognition: 14th International …, 2018 | 2 | 2018 |
| Scalable machine learning approaches for chromatographic pattern extraction in large-scale mass spectrometry data O Permiakova Université Grenoble Alpes [2020-....], 2021 | | 2021 |
| Package ‘chickn’ O Permiakova, R Guibert, T Burger | | 2020 |
