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BORG Current Research List

KAUST CEMSE BORG Research Complex Variant prioritization CompleX: Variant prioritization in complex disease Phenotype-based methods have repeatedly shown to be highly effective in identifying causative variants in whole genome or whole exome sequences. The main limitation of phenotype-based methods, however, is the limited availability of characterised genotype-phenotype associations. Model organism phenotypes have in the past been used to supplement genotype–phenotype associations observed in humans and were demonstrated to predict disease genes. Nevertheless, in almost all cases, genotypes

Data

​Name Description​ Publication​ AberOWL Ontology repository and reasoning-as-a-service Hoehndorf, R., Slater, L., Schofield, P. N., & Gkoutos, G. V. (2015). Aber-OWL: a framework for ontology-based data access in biology. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0456-9 PhenomeNET Cross-species phenotype ontology and similarity computation Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2011). PhenomeNET: a whole-phenome approach to disease gene discovery. Nucleic Acids Research, 39(18), e119–e119. https://doi.org/10.1093/nar/gkr538 PathoPhenoDB Database of pathogen-to

Neuro-symbolic systems

Details Technological breakthroughs in biological and biomedical research have led to the generation of a large amount of data in many areas. Utilizing this data for improving human life is nevertheless a challenge due to the inherent heterogeneity in the data, the complexity, and the large data size. The early promises of the success of next-generation sequencing in biomedicine have fallen short because it proved challenging to interpret the information obtained from individuals and translate it into new discoveries that benefit human health. A key challenge in biomedicine is to understand

Ontologies

​Name ​Description Publication​ ​ FLOPO ​Flora Phenotype Ontology for traits observed in flowering plants ​ https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0107-8 ​ DermO ​Dermatological Disease Ontology ​https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0085-x ​ NBO ​Neuro-Behavior Ontology for behavioral processes and phenotypes ​ https://linkinghub.elsevier.com/retrieve/pii/B978-0-12-388408-4.00004-6 ​ PhenomeNET ​Cross-species phenotype ontology for human, mouse, and fish ​https://academic.oup.com/nar/article/39/18/e119/1092588 ​ PATO ​Ontology of qualities

Pathogen informatics

Details Infectious diseases are caused by a wide range of organisms (viruses, bacteria, fungi, worms, protozoa) that are generally considered as pathogens. Antimicrobial drugs are often the first-line therapy for infectious diseases. However, drug resistance accumulates over time due to the selection of genetic changes in pathogen populations when they are exposed to antimicrobial drugs (such as antibiotics, antifungals, antivirals, antimalarials, and antihelmintics). It now becomes crucial to develop strategies that can identify a pathogen rapidly and determine successful treatment options

Phenotypes

CompleX: Variant prioritization in complex disease Details We are now at a stage when the discovery of new disease genes is slowing. The number of patients remaining undiagnosed following whole exome sequencing argues either that many more disease genes and variants still await discovery, or that the heterogeneity and novelty of disease phenotypes that we see are due to a combination of alleles of multiple, known, disease genes in the same individual. While the particular combinations of alleles in the same person may be rare, they likely involve medium-rare or common alleles as well as rare

Prediction of functions and phenotypes

Details The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high throughput sequencing technologies in the life sciences. It is a key question in the life sciences to identify the functions of proteins, and furthermore to identify the phenotypes that may be associated with a loss (or gain) of function in these proteins. Protein functions are generally determined experimentally, and it is clear that experimental determination of protein functions will not scale to the current – and rapidly increasing – amount of

Software

​Name ​Description Publication​ DTI-Voodoo Accurate prediction of drug--target interactions through graph neural networks and side-effects Hinnerichs, T., & Hoehndorf, R. (2021). DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions. Bioinformatics, 37(24), 4835–4843. https://doi.org/10.1093/bioinformatics/btab548 MESH2OWL Converts the MESH metathesaurus into OWL Onto2Graph Generating graphs from ontology axioms Rodríguez-García, M. Á., & Hoehndorf, R. (2018). Inferring ontology graph structures using OWL reasoning. BMC

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Contacts

Robert Hoehndorf Interim Associate Director, Computational Bioscience Research Center Associate Professor, Computer Science Principal Investigator, Bio-Ontology Research Group Robert is an Associate Professor in Computer Science and the PI of the Bio-Ontology Research Group (BORG). Prior to joining KAUST, Robert held research positions at the Max Planck Institute for Evolutionary Anthropology, the European Bioinformatics Institute, University of Cambridge, and Aberystwyth University. He earned his PhD degree in Computer Science from the University of Leipzig. Robert's research focuses on the

Join Us

Postdoctoral research in neuro-symbolic AI Overview: We are seeking to fill a position for a postdoctoral researchers at the Computational Bioscience Research Center at King Abdullah University of Science and Technology. The researcher will work in the group of Robert Hoehndorf (http://borg.kaust.edu.sa) on developing neuro-symbolic methods in AI, in particular focusing on using neural methods with Description Logic and Semantic Web technologies. Start date: Earliest possible Location: King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Employment: Full-time Duration

Publications

Teaching

Teaching

Introduction to Artificial Intelligence The course provided a broad overview over the field of Artificial Intelligence and introduce the basic methods and principles used to design intelligent systems. A key emphasis is on problem-solving and decision making as two key aspects of intelligent behavior, as well as on logical foundations of Artificial Intelligence. ​ ​​Prerequisites: Knowledge in discrete mathematics, in particular, set theory and complexity theory (e.g., CS260 or related experience in undergraduate studies). Knowledge Representation & Reasoning (CS213) The course covers basic

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Bio-Ontology Research Group

Professor Robert Hoehndorf leads the so-called BORG research group, which stands for Bio-Ontology Research Group numerical analysis, partial differential equations Welcome to the website of the Bio-Ontology Research Group at KAUST. Our research focuses on the use of bio-ontologies for data integration and analysis in biology. We are interested in biological problems that require integration of multiple types of data and integration of data across scales and levels of granularity. We work on the development of biomedical ontologies, their application to data integration and annotation, and