| Protein–protein binding affinity prediction on a diverse set of structures IH Moal, R Agius, PA Bates Bioinformatics 27 (21), 3002-3009, 2011 | 124 | 2011 |
| Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions R Moretti, SJ Fleishman, R Agius, M Torchala, PA Bates, PL Kastritis, ... Proteins: Structure, Function, and Bioinformatics 81 (11), 1980-1987, 2013 | 111 | 2013 |
| Machine learning can identify newly diagnosed patients with CLL at high risk of infection R Agius, C Brieghel, MA Andersen, AT Pearson, B Ledergerber, ... Nature communications 11 (1), 363, 2020 | 91 | 2020 |
| Understanding cancer mechanisms through network dynamics TMK Cheng, S Gulati, R Agius, PA Bates Briefings in functional genomics 11 (6), 543-560, 2012 | 47 | 2012 |
| A Markov‐chain model description of binding funnels to enhance the ranking of docked solutions M Torchala, IH Moal, RAG Chaleil, R Agius, PA Bates Proteins: Structure, Function, and Bioinformatics 81 (12), 2143-2149, 2013 | 38 | 2013 |
| Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization R Agius, M Torchala, IH Moal, J Fernández-Recio, PA Bates PLOS Computational Biology 9 (9), e1003216, 2013 | 31 | 2013 |
| Artificial intelligence models in chronic lymphocytic leukemia–recommendations toward state-of-the-art R Agius, M Parviz, CU Niemann Leukemia & Lymphoma 63 (2), 265-278, 2022 | 10 | 2022 |
| Early stimulated immune responses predict clinical disease severity in hospitalized COVID-19 patients R Svanberg, C MacPherson, A Zucco, R Agius, T Faitova, MA Andersen, ... Communications medicine 2 (1), 114, 2022 | 8 | 2022 |
| Identifying patients with chronic lymphocytic leukemia without need of treatment: End of endless watch and wait? C Brieghel, V Galle, R Agius, C da Cunha‐Bang, MA Andersen, ... European Journal of Haematology 108 (5), 369-378, 2022 | 8 | 2022 |
| Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para) clinical data M Parviz, C Brieghel, R Agius, CU Niemann Blood advances 6 (12), 3716-3728, 2022 | 6 | 2022 |
| Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning AG Zucco, R Agius, R Svanberg, KS Moestrup, RZ Marandi, ... Scientific Reports 12 (1), 13879, 2022 | 3 | 2022 |
| P629: THE CLL TREATMENT INFECTION MODEL–CLINICAL PROSPECTIVE VALIDATION AS PART OF THE PREVENT-ACALL TRIAL C Niemann, MD Levin, A Österborg, J Lundin, M Kättström, J Vollerup, ... HemaSphere 7 (S3), e1432517, 2023 | 1 | 2023 |
| Identifying CLL patients at high risk of atrial fibrillation on treatment using machine learning M Parviz, R Agius, EC Rotbain, N Vainer, K Aarup, CU Niemann Leukemia & Lymphoma, 1-11, 2024 | | 2024 |
| The Danish Lymphoid Cancer Research (DALY-CARE) data resource: the basis for developing data-driven hematology C Brieghel, M Werling, CM Frederiksen, M Parviz, C da Cunha-Bang, ... medRxiv, 2024.04. 11.24305663, 2024 | | 2024 |
| Author Correction: Early stimulated immune responses predict clinical disease severity in hospitalized COVID-19 patients R Svanberg, C MacPherson, A Zucco, R Agius, T Faitova, MA Andersen, ... Communications Medicine 3 (1), 15, 2023 | | 2023 |
| Identifying CLL Patients at High Risk of Infection on Treatment Using Machine Learning M Parviz, R Agius, E Curovic Rotbain, K Aarup, N Vainer, CU Niemann Blood 140 (Supplement 1), 7034-7035, 2022 | | 2022 |
| Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning (preprint) AG Zucco, R Agius, R Svanberg, KS Moestrup, RZ Marandi, ... | | 2021 |
| Understanding Stability of Protein-Protein Complexes R Agius UCL (University College London), 2015 | | 2015 |
| Implementation of the CLL Treatment Infection Model Adjoined to an Electronic Health Record System-Guidelines for Practical Implementation of Data-Driven Models R Agius, AC Riis-Jensen, B Wimmer, C da Cunha-Bang, DD Murray, ... Available at SSRN 4555192, 0 | | |
