| A framework for implementing machine learning on omics data G Dubourg-Felonneau, T Cannings, F Cotter, H Thompson, N Patel, ... arXiv preprint arXiv:1811.10455, 2018 | 8 | 2018 |
| System for identifying and developing individual naturally-occurring proteins as food ingredients by machine learning and database mining combined with empirical testing for a … J Hume, G Dubourg-felonneau, A Kunibe, L Lee US Patent 11,439,159, 2022 | 5 | 2022 |
| Methods and Systems for Nucleic Acid Variant Detection and Analysis G Dubourg-felonneau, L Harries, H Clifford, N Patel US Patent App. 16/752,240, 2020 | 5 | 2020 |
| Improving protein subcellular localization prediction with structural prediction & graph neural networks G Dubourg-Felonneau, A Abbasi, E Akiva, L Lee bioRxiv, 2022.11. 29.518403, 2022 | 3 | 2022 |
| SomaticNET: Neural network evaluation of somatic mutations in cancer G Dubourg-Felonneau, D Rebergen, C Parsons, H Thompson, ... Annals of Oncology 30 (Supplement_5), 2019 | 3 | 2019 |
| Protein Organization with Manifold Exploration and Spectral Clustering G Dubourg-Felonneau, S Shams, E Akiva, L Lee bioRxiv, 2021.12. 08.471858, 2021 | 2 | 2021 |
| Flatsomatic: A method for compression of somatic mutation profiles in cancer G Dubourg-Felonneau, Y Kussad, D Kirkham, JW Cassidy, N Patel, ... arXiv preprint arXiv:1911.13259, 2019 | 2 | 2019 |
| Learning embeddings from cancer mutation sets for classification tasks G Dubourg-Felonneau, Y Kussad, D Kirkham, JW Cassidy, N Patel, ... arXiv preprint arXiv:1911.09008, 2019 | 2 | 2019 |
| Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer G Dubourg-Felonneau, O Darwish, C Parsons, D Rebergen, JW Cassidy, ... arXiv preprint arXiv:1912.04174, 2019 | 1 | 2019 |
| Effective sub-clonal cancer representation to predict tumor evolution A Akbar, G Dubourg-Felonneau, A Solovyev, JW Cassidy, N Patel, ... arXiv preprint arXiv:1911.12774, 2019 | 1 | 2019 |
| Interlacing personal and reference genomes for machine learning disease-variant detection LR Harries, S Zhang, G Dubourg-Felonneau, JHR Farmery, J Sinai, ... arXiv preprint arXiv:1811.11674, 2018 | 1 | 2018 |
| Selecting food ingredients from vector representations of individual proteins using cluster analysis and precision fermentation E Akiva, G Dubourg-felonneau, A Kunibe, L Lee, J Hume US Patent App. 18/473,018, 2024 | | 2024 |
| Sustainable manufacture of foods and cosmetics by computer enabled discovery and testing of individual protein ingredients J Hume, G Dubourg-felonneau, A Kunibe, L Lee US Patent 11,805,791, 2023 | | 2023 |
| Flywheel discovery system that twins machine learning with high-throughput expression and laboratory analysis to identify and develop individual proteins as food ingredients J Hume, G Dubourg-felonneau, A Kunibe, L Lee US Patent App. 17/943,207, 2023 | | 2023 |
| PiNUI: A Dataset of Protein-Protein Interactions for Machine Learning G Dubourg-Felonneau, DM Wesego, E Akiva, R Varadan bioRxiv, 2023.12. 12.571298, 2023 | | 2023 |
| A Framework for Implementing Machine Learning on Omics Data T Cannings, G Dubourg-Felonneau, F Cotter, H Thompson, N Patel, ... ML4H: Machine Learning for Health: ML4H 2018: a workshop at NeurIPS 2018, 2018 | | 2018 |
