Professor Hoehndorf is interested in artificial intelligence, knowledge representation, biomedical informatics, ontology.
Selected Publications
Hoehndorf, R., Queralt-Rosinach, N., “Data science and symbolic AI: Synergies, challenges and opportunities”. In: Data Science.
Boudellioua, I., Mahamad Razali, R. B., Kulmanov, M., Hashish, Y., Bajic, V. B., Goncalves- Serra, E., Schoenmakers, N., Gkoutos, G. V., Schofield, P. N., Hoehndorf, R., “Semantic prioritization of novel causative genomic variants”. In: PLOS Computational Biology 13.4 (Apr. 2017), pp. 1–21.
Hoehndorf, R., Schofield, P. N., Gkoutos, G. V., “Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases”. In: Scientific Reports 5 (June 2015), p. 10888.
Robert Hoehndorf, Tanya Hiebert, Nigel W. Hardy, Paul N. Schofield, Georgios V. Gkoutos, and Michel Dumontier. "Mouse model phenotypes provide information about human drug targets". In: Bioinformatics (Oct. 2013).
Robert Hoehndorf, Paul N. Schofield, and Georgios V. Gkoutos. "PhenomeNET: a whole-phenome approach to disease gene discovery". In: Nucleic Acids Research 39.18 (July 2011), e119.