| An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat NF Grinberg, OI Orhobor, RD King Machine Learning 109 (2), 251-277, 2020 | 113 | 2020 |
| Cross-validation is safe to use RD King, OI Orhobor, CC Taylor Nature Machine Intelligence 3 (4), 276-276, 2021 | 39 | 2021 |
| Large-scale assessment of deep relational machines T Dash, A Srinivasan, L Vig, OI Orhobor, RD King Inductive Logic Programming: 28th International Conference, ILP 2018 …, 2018 | 28 | 2018 |
| Batched bayesian optimization for drug design in noisy environments H Bellamy, AA Rehim, OI Orhobor, R King Journal of Chemical Information and Modeling 62 (17), 3970-3981, 2022 | 21 | 2022 |
| Transformational machine learning: Learning how to learn from many related scientific problems I Olier, OI Orhobor, T Dash, AM Davis, LN Soldatova, J Vanschoren, ... Proceedings of the National Academy of Sciences 118 (49), e2108013118, 2021 | 20 | 2021 |
| Federated ensemble regression using classification OI Orhobor, LN Soldatova, RD King Discovery Science: 23rd International Conference, DS 2020, Thessaloniki …, 2020 | 5 | 2020 |
| Discovery of genomic variants associated with genebank historical traits for rice improvement: SNP and indel data, phenotypic data, and GWAS results MD Sanciangco, NN Alexandrov, D Chebotarov, RD King, ... Harvard Dataverse 2, 2018 | 5 | 2018 |
| Predicting rice phenotypes with meta and multi-target learning OI Orhobor, NN Alexandrov, RD King Machine Learning 109 (11), 2195-2212, 2020 | 4 | 2020 |
| Generating explainable and effective data descriptors using relational learning: application to cancer biology OI Orhobor, J French, LN Soldatova, RD King International Conference on Discovery Science, 374-385, 2020 | 3 | 2020 |
| Beating the best: improving on AlphaFold2 at protein structure prediction A Abdel-Rehim, O Orhobor, H Lou, H Ni, RD King arXiv preprint arXiv:2301.07568, 2023 | 2 | 2023 |
| A simple spatial extension to the extended connectivity interaction features for binding affinity prediction OI Orhobor, AA Rehim, H Lou, H Ni, RD King Royal Society Open Science 9 (5), 211745, 2022 | 2 | 2022 |
| Predicting rice phenotypes with meta-learning OI Orhobor, NN Alexandrov, RD King Discovery Science: 21st International Conference, DS 2018, Limassol, Cyprus …, 2018 | 2 | 2018 |
| Imbalanced regression using regressor-classifier ensembles OI Orhobor, NF Grinberg, LN Soldatova, RD King Machine Learning 112 (4), 1365-1387, 2023 | 1 | 2023 |
| Parallel Constraint-Driven Inductive Logic Programming A Cropper, O Orhobor, C Dinu, R Morel arXiv preprint arXiv:2109.07132, 2021 | 1 | 2021 |
| A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza sativa) OI Orhobor PQDT-Global, 2019 | 1 | 2019 |
| Transformative machine learning I Olier, OI Orhobor, J Vanschoren, RD King arXiv preprint arXiv:1811.03392, 2018 | 1 | 2018 |
| Extension of Transformational Machine Learning: Classification Problems A Mahmud, O Orhobor, RD King arXiv preprint arXiv:2309.16693, 2023 | | 2023 |
| Protein–ligand binding affinity prediction exploiting sequence constituent homology A Abdel-Rehim, O Orhobor, L Hang, H Ni, RD King Bioinformatics 39 (8), btad502, 2023 | | 2023 |
| Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability OI Orhobor, NN Alexandrov, D Chebotarov, T Kretzschmar, KL McNally, ... bioRxiv, 805002, 2019 | | 2019 |
| PANDA: a framework for reasoning with scientific Uncertainty LN Soldatova, OI Orhobor, J French, RD King | | |
Ross D. KingProfessor of Machine Intelligence, Chalmers UniversityVerified email at chalmers.se
