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DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

Year: 2017 Venue: Bioinformatics Authors: Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf Abstract Motivation: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes

DeepGOMeta

DeepGO trained specifically for metagenomic communities; predicts functional roles of proteins recovered from environmental samples and links them to biogeochemical processes. Get it GitHub: https://github.com/bio-ontology-research-group/deepgometa ★ 8 Developed in projects Enabling desert revegetation by AI-tailored soil microbiome fortification Enabling mangrove restoration by AI-tailored microbiome fortification Category: Protein Function Prediction

DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction

Year: 2024 Venue: Scientific Reports Authors: Rund Tawfiq, Kexin Niu, Robert Hoehndorf, Maxat Kulmanov DOI: 10.1038/s41598-024-82956-w Abstract AbstractAnalyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have

DeepGOPlus

CNN-ensemble protein-function predictor that augments sequence-based scoring with k-nearest-neighbour homology and GO axioms; one of the strongest CAFA-evaluated open models. Get it GitHub: https://github.com/bio-ontology-research-group/deepgoplus ★ 103 Homepage: https://deepgo.cbrc.kaust.edu.sa/deepgo/ Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Bio2Vec: Smart analytics infrastructure for the life sciences Category: Protein Function Prediction

DeepGOPlus: improved protein function prediction from sequence

Year: 2020 Venue: Bioinformatics Authors: Maxat Kulmanov, Robert Hoehndorf Abstract Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein–protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

Year: 2021 Venue: Nucleic Acids Research Authors: Maxat Kulmanov, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/nar/gkab373 Abstract Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

Year: 2020 Venue: PLOS Computational Biology Authors: Maxat Kulmanov, Robert Hoehndorf DOI: 10.1371/journal.pcbi.1008453 Abstract Author summary Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression

DeepPVP: phenotype-based prioritization of causative variants using deep learning

Year: 2019 Venue: BMC Bioinformatics Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient{

DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes

Year: 2021 Venue: Bioinformatics Authors: Wang Liu-Wei, Senay Kafkas, Jun Chen, Nicholas J Dimonaco, Jesper Tegner, Robert Hoehndorf DOI: 10.1093/bioinformatics/btab147 Abstract Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms

DELE

Deductive EL embeddings: enrich training data with the deductive closure of an ontology before learning, so embeddings recover entailment rather than only asserted axioms. Get it GitHub: https://github.com/bio-ontology-research-group/DELE ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

DELE: Deductive EL++ Embeddings for Knowledge Base Completion

Year: 2026 Venue: Neurosymbolic Artificial Intelligence Authors: Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732261420011 Abstract Ontology embeddings map classes, roles, and individuals in ontologies into R^n , and within R^n similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL^++ , several optimization-based embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not distinguish between statements that are

DermO; an ontology for the description of dermatologic disease

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Hannah M. Fisher, Robert Hoehndorf, Bruno S. Bazelato, Soheil S. Dadras, Lloyd E. King, Georgios V. Gkoutos, John P. Sundberg, Paul N. Schofield DOI: 10.1186/s13326-016-0085-x Abstract There have been repeated initiatives to produce standard nosologies and terminologies for cutaneous disease, some dedicated to the domain and some part of bigger terminologies such as ICD-10. Recently, formally structured terminologies, ontologies, have been widely developed in many areas of biomedical research. Primarily, these address the aim of

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species

Year: 2017 Venue: Scientific Reports Authors: Adil Salhi, Sonia Negrao, Magbubah Essack, Mitchell J. L. Morton, Salim Bougouffa, Rozaimi Mohamad Razali, Aleksandar Radovanovic, Benoit Marchand, Maxat Kulmanov, Robert Hoehndorf, Mark A. Tester, Vladimir B. Bajic Abstract Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and

DESM: portal for microbial knowledge exploration systems

Year: 2016 Venue: Nucleic Acids Research Authors: Adil Salhi, Magbubah Essack, Aleksandar Radovanovic, Benoit Marchand, Salim Bougouffa, Andre Antunes, Marta Filipa Simoes, Feras F. Lafi, Olaa A. Motwalli, Ameerah Bokhari, Tariq Malas, Soha Al Amoudi, Ghofran Othum, Intikhab Allam, Katsuhiko Mineta, Xin Gao, Robert Hoehndorf, John A. C. Archer, Takashi Gojobori, Vladimir B. Bajic DOI: 10.1093/nar/gkv1147 Abstract Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about

Drug mechanisms

Pharmacology sits at the intersection of molecules, cells, organs, and patient outcomes, and understanding the mechanism of a drug requires reasoning across all of these scales. We apply ontologies, knowledge graphs, and semantic representations to model drug-target interactions, drug indications, and adverse drug reactions, linking molecular biology to systems-level physiology through causal structures over biomedical knowledge. The aim is to move beyond statistical associations to mechanistic, machine-readable models of drug action that can support repurposing, anticipate adverse effects

EL Embeddings

Reference implementation of geometric embeddings for the EL++ description logic, the predecessor of GeometrE and BoxSquaredEL. Preserves subsumption reasoning by mapping classes to convex regions. Get it GitHub: https://github.com/bio-ontology-research-group/el-embeddings ★ 28 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

EL Embeddings: Geometric construction of models for the Description Logic EL++

Year: 2019 Venue: Proceedings of IJCAI 2019 Authors: Maxat Kulmanov, Wang Liu-Wei, Yuan Yan, Robert Hoehndorf Abstract An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic EL++ that are also models of the TBox. To find such embeddings, we define an

EMC10 homozygous variant identified in a family with global developmental delay, mild intellectual disability, and speech delay

Year: 2020 Venue: Clinical Genetics Authors: Muhammad Umair, Mariam Ballow, Abdulaziz Asiri, Yusra Alyafee, Abeer Tuwaijri, Kheloud M. Alhamoudi, Taghrid Aloraini, Marwa Abdelhakim, Azza Thamer Althagafi, Senay Kafkas, Lamia Alsubaie, Muhammad Talal Alrifai, Robert Hoehndorf, Ahmed Alfares, Majid Alfadhel DOI: 10.1111/cge.13842 Abstract In recent years, several genes have been implicated in the variable disease presentation of global developmental delay (GDD) and intellectual disability (ID). The endoplasmic reticulum membrane protein complex (EMC) family is known to be involved in GDD and ID

Enhancing Geometric Ontology Embeddings for ^++ with Negative Sampling and Deductive Closure Filtering

Year: 2024 Venue: Neural-Symbolic Learning and Reasoning Authors: Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-031-71167-1_18 Abstract Ontology embeddings map classes, relations, and individuals in ontologies into \(\mathbb {R}^n\) , and within \(\mathbb {R}^n\) similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic \(\mathcal{E}\mathcal{L}^{++}\) , several embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not

Enriched biodiversity data as a resource and service

Year: 2014 Venue: Biodiversity Data Journal Authors: Rutger Vos, Jordan Biserkov, Bachir Balech, Niall Beard, Matthew Blissett, Christian Brenninkmeijer, Tom van Dooren, David Eades, George Gosline, Quentin Groom, Thomas Hamann, Hannes Hettling, Robert Hoehndorf, Ayco Holleman, Peter Hovenkamp, Patricia Kelbert, David King, Don Kirkup, Youri Lammers, Thibaut DeMeulemeester, Daniel Mietchen, Jeremy Miller, Ross Mounce, Nicola Nicolson, Rod Page, Aleksandra Pawlik, Serrano Pereira, Lyubomir Penev, Kevin Richards, Guido Sautter, David Shorthouse, Marko Tahtinen, Claus Weiland, Alan Williams

Evaluating Different Methods for Semantic Reasoning Over Ontologies

Year: 2023 Venue: Joint Proceedings of Scholarly QALD 2023 and SemREC 2023 co-located with 22nd International Semantic Web Conference ISWC 2023, Athens, Greece, November 6-10, 2023 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf Abstract Reasoning over knowledge bases such as Semantic Web ontologies enables the discovery of new facts from existing knowledge. Knowledge-enhanced machine learning has motivated the development of neuro-symbolic reasoners, which enable faster but approximate computation of new facts or entailments. Neuro-symbolic methods generate vector representations

Evaluating semantic similarity methods for comparison of text-derived phenotype profiles

Year: 2022 Venue: BMC Medical Informatics and Decision Making Authors: Luke T. Slater, Sophie Russell, Silver Makepeace, Alexander Carberry, Andreas Karwath, John A. Williams, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1186/s12911-022-01770-4 Abstract Abstract Background Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance ‘patient-like me’ analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work

Evaluating the effect of annotation size on measures of semantic similarity

Year: 2016 Venue: Proceedings of Bio-Ontologies SIG Authors: Maxat Kulmanov, Robert Hoehndorf Abstract Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products. Here, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of

Experiences with Aber-OWL, an Ontology Repository with OWL EL Reasoning

Year: 2016 Venue: Ontology Engineering: 12th International Experiences and Directions Workshop on OWL, OWLED 2015, co-located with ISWC 2015, Bethlehem, PA, USA, October 9-10, 2015, Revised Selected Papers Authors: Luke Slater, Miguel Rodriguez-Garcia, Keiron O'Shea, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf DOI: 10.1007/978-3-319-33245-1_8 Topics Applied Ontology · Ontology engineering

Exploring the Use of Ontology Components for Distantly-Supervised Disease and Phenotype Named Entity Recognition

Year: 2023 Venue: Proceedings of the International Conference on Biomedical Ontologies 2023 together with the Workshop on Ontologies for Infectious and Immune-Mediated Disease Data Science (OIIDDS 2023) and the FAIR Ontology Harmonization and TRUST Data Interoperability Workshop (FOHTI 2023), Bras\', Brazil, August 28 - September 1, 2023 Authors: Sumyyah Toonsi, Senay Kafkas, Robert Hoehndorf Abstract The lack of curated corpora is one of the major obstacles for Named Entity Recognition (NER). With the advancements in deep learning and development of robust language models, distant supervision

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Jerven T. Bolleman, Christopher J. Mungall, Francesco Strozzi, Joachim Baran, Michel Dumontier, Raoul P. J. Bonnal, Robert Buels, Robert Hoehndorf, Takatomo Fujisawa, Toshiaki Katayama, Peter A. J. Cock DOI: 10.1186/s13326-016-0067-z Abstract Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

Year: 2020 Venue: Bioinformatics Authors: Fatima Z. Smaili, Xin Gao, Robert Hoehndorf Abstract Motivation: There are now over 500 ontologies in the life sciences. Over the past years, significant resources have been invested into formalizing these biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. At the same time, ontologies have extended their

From Axioms over Graphs to Vectors, and Back Again: Evaluating the Properties of Graph-based Ontology Embeddings

Year: 2023 Venue: Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf Abstract Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structure, i.e., introducing a set of nodes and edges for named entities and logical axioms, and then applying a graph embedding to

Fully Geometric Multi-hop Reasoning on Knowledge Graphs with Transitive Relations

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-032-25156-5_14 Abstract Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space. Current geometric embedding methods show to be useful on this task by mapping entities to geometric regions and logical operations to latent transformations. While a geometric embedding can provide a direct interpretability framework for query answering, current methods have only leveraged the geometric construction of entities, failing to

Genomic diversity and antimicrobial resistance of Staphylococcus aureus in Saudi Arabia: a nationwide study using whole-genome sequencing

Year: 2025 Venue: Microbial Genomics Authors: Mohammed S. Alarawi, Musaad Altammami, Mohammed Abutarboush, Maxat Kulmanov, Dalal M. Alkuraithy, Senay Kafkas, Robert Radley, Marwa Abdelhakim, Hind Aldakhil, Reema A. Bawazeer, Mohammed A. Alolayan, Basel M. Alnafjan, Abdulaziz A. Huraysi, Amani Almaabadi, Bandar A. Suliman, Areej G. Aljohani, Hassan A. Hemeg, Mohammed S. Almogbel, Meshari Alazmi, Abdulrahman S. Bazaid, Turki S. Abujamel, Anwar M. Hashem, Ibrahim A. Al-Zahrani, Mohammed S. Abdoh, Haya I. Hobani, Rakan F. Felemban, Wafaa A. Alhazmi, Pei-Ying Hong, Majed F. Alghoribi, Sameera

Genomic landscape in Saudi patients with hepatocellular carcinoma using whole-genome sequencing: a pilot study

Year: 2023 Venue: Frontiers in Gastroenterology Authors: Mazen Hassanain, Yang Liu, Weam Hussain, Albandri Binowayn, Duna Barakeh, Ebtehal Alsolme, Faisal AlSaif, Ghaida Almasaad, Mohammed AlSwayyed, Maram Alaqel, Rana Aljunidel, Sherin Abdelrahman, Charlotte A. E. Hauser, Saleh Alqahtani, Robert Hoehndorf, Malak Abedalthagafi DOI: 10.3389/fgstr.2023.1205415 Abstract Our findings indicate that most of the HCC patients possess cancer-related genetic variants, and the altered pathways in these patients exhibit similarities. Notably, resistant patients exhibit a higher frequency of aberrations in

Genomic landscape of retinoblastoma: Insights into risk stratification and precision pediatric Neuro-Oncology

Year: 2025 Venue: Neuro-Oncology Pediatrics Authors: Azza Maktabi, Yang Liu, Saleh Almesfer, Marwa Abdelhakim, Hind Aldakhil, Maxat Kulmanov, Deepak P Edward, Robert Hoehndorf, Malak Abedalthagafi DOI: 10.1093/neuped/wuaf017 Abstract Abstract Background Retinoblastoma is the most common intraocular malignancy of childhood, yet its genomic landscape remains incompletely defined, particularly in understudied populations. Beyond RB1 loss, the contribution of additional somatic and germline alterations to disease heterogeneity and clinical behavior is unclear. Methods We performed whole-exome

Genomics

Our genomics work develops resources and computational methods for population-scale genome analysis, with a particular focus on populations that have historically been under-represented in reference databases. Within the BORG group at KAUST's Computer Science Program, we build reference genome assemblies, pangenome graphs for the Saudi and wider Middle Eastern population, variant-calling and structural-variant pipelines, and analyses of antimicrobial resistance from whole-genome sequencing. Our distinctive angle is the tight integration of formal phenotype and function knowledge with sequence

GFVO: the Genomic Feature and Variation Ontology

Year: 2015 Venue: PeerJ Authors: Joachim Baran, Bibi Sehnaaz Begum Durgahee, Karen Eilbeck, Erick Antezana, Robert Hoehndorf, Michel Dumontier DOI: 10.7717/peerj.933 Abstract Falling costs in genomic laboratory experiments have led to a steady increase of genomic feature and variation data. Multiple genomic data formats exist for sharing these data, and whilst they are similar, they are addressing slightly different data viewpoints and are consequently not fully compatible with each other. The fragmentation of data format specifications makes it hard to integrate and interpret data for further