Maxat Kulmanov
Cited by
Cited by
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
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
DeepGOPlus: improved protein function prediction from sequence
M Kulmanov, R Hoehndorf
Bioinformatics 37 (8), 1187, 2021
Semantic similarity and machine learning with ontologies.
M Kulmanov, FZ Smaili, X Gao, R Hoehndorf
Oxford University Press (OUP), 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
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
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
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
Semantic prioritization of novel causative genomic variants
I Boudellioua, RBM Razali, M Kulmanov, Y Hashish, VB Bajic, ...
PLoS computational biology 13 (4), e1005500, 2017
Evaluating the effect of annotation size on measures of semantic similarity
M Kulmanov, R Hoehndorf
Journal of biomedical semantics 8 (1), 1-10, 2017
DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier
M Kulmanov, R Hoehndorf
PLoS computational biology, 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
DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web
M Kulmanov, F Zhapa-Camacho, R Hoehndorf
Oxford University Press (OUP), 2021
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
Description Logic EL++ Embeddings with Intersectional Closure
X Peng, Z Tang, M Kulmanov, K Niu, R Hoehndorf
arXiv preprint arXiv:2202.14018, 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
DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species
A Salhi, S Negrăo, M Essack, MJL Morton, S Bougouffa, R Razali, ...
Scientific reports 7 (1), 5968, 2017
mOWL: Python library for machine learning with biomedical ontologies
F Zhapa-Camacho, M Kulmanov, R Hoehndorf
Oxford University Press (OUP), 2022
Ontology-based validation and identification of regulatory phenotypes
M Kulmanov, PN Schofield, GV Gkoutos, R Hoehndorf
Bioinformatics 34 (17), 2018
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
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