| DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier M Kulmanov, MA Khan, R Hoehndorf Bioinformatics 34 (4), 660-668, 2018 | 503 | 2018 |
| DeepGOPlus: improved protein function prediction from sequence M Kulmanov, R Hoehndorf Bioinformatics 37 (8), 1187, 2021 | 447 | 2021 |
| The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, ... Genome Biology 20 (244), 2019 | 428 | 2019 |
| Semantic similarity and machine learning with ontologies. M Kulmanov, FZ Smaili, X Gao, R Hoehndorf Oxford University Press (OUP), 2020 | 227* | 2020 |
| EL Embeddings: Geometric construction of models for the Description Logic EL++ M Kulmanov, W Liu-Wei, Y Yan, R Hoehndorf International Joint Conferences on Artificial Intelligence Organization …, 2019 | 132 | 2019 |
| DeepPVP: phenotype-based prioritization of causative variants using deep learning I Boudellioua, M Kulmanov, PN Schofield, GV Gkoutos, R Hoehndorf BMC Bioinformatics 29 (65), 2019 | 82 | 2019 |
| Functional pangenome analysis shows key features of e protein are preserved in sars and sars-cov-2 I Alam, AA Kamau, M Kulmanov, Ł Jaremko, ST Arold, A Pain, T Gojobori, ... Frontiers in cellular and infection microbiology 10, 405, 2020 | 71* | 2020 |
| DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms M Kulmanov, R Hoehndorf Bioinformatics 38 (Supplement_1), Pages i238–i245,, 2022 | 53 | 2022 |
| Semantic prioritization of novel causative genomic variants I Boudellioua, RBM Razali, M Kulmanov, Y Hashish, VB Bajic, ... PLoS computational biology 13 (4), e1005500, 2017 | 39 | 2017 |
| Protein function prediction as approximate semantic entailment M Kulmanov, FJ Guzmán-Vega, P Duek Roggli, L Lane, ST Arold, ... Nature Machine Intelligence 6 (2), 220–228, 2024 | 35* | 2024 |
| DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web M Kulmanov, F Zhapa-Camacho, R Hoehndorf Oxford University Press (OUP), 2021 | 35 | 2021 |
| Evaluating the effect of annotation size on measures of semantic similarity M Kulmanov, R Hoehndorf Journal of biomedical semantics 8 (1), 1-10, 2017 | 35 | 2017 |
| DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier M Kulmanov, R Hoehndorf PLoS computational biology, 2020 | 25 | 2020 |
| PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research Ş Kafkas, M Abdelhakim, Y Hashish, M Kulmanov, M Abdellatif, ... Scientific data 6 (1), 1-8, 2019 | 22 | 2019 |
| mOWL: Python library for machine learning with biomedical ontologies F Zhapa-Camacho, M Kulmanov, R Hoehndorf Oxford University Press (OUP), 2022 | 19 | 2022 |
| OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants I Boudellioua, M Kulmanov, PN Schofield, GV Gkoutos, R Hoehndorf Scientific Reports 8 (1), 2045-2322, 2018 | 19 | 2018 |
| Description Logic EL++ Embeddings with Intersectional Closure X Peng, Z Tang, M Kulmanov, K Niu, R Hoehndorf arXiv preprint arXiv:2202.14018, 2022 | 18 | 2022 |
| DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration S Althubaiti, M Kulmanov, Y Liu, G Gkoutos, P Schofield, R Hoehndorf bioRxiv, 2021 | 13 | 2021 |
| DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species A Salhi, S Negrão, M Essack, MJL Morton, S Bougouffa, R Razali, ... Scientific reports 7 (1), 5968, 2017 | 11 | 2017 |
| Ontology-based validation and identification of regulatory phenotypes M Kulmanov, PN Schofield, GV Gkoutos, R Hoehndorf Bioinformatics 34 (17), 2018 | 7 | 2018 |
Robert HoehndorfKing Abdullah University of Science and Technologykaust.edu.sa의 이메일 확인됨