1. Smedley D, Jacobsen JO, Jäger M, Köhler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL, Bone WP, Haendel MA, Robinson PN. (2015). Next-generation diagnostics and disease-gene discovery with the Exomiser. Nature Protocols, . Link
  2. Bone WP, Washington NL, Buske OJ, Adams DR, Davis J, Draper D, Flynn ED, Girdea M, Godfrey R, Golas G, Groden C, Jacobsen J, Köhler S, Lee EM, Links AE, Markello TC, Mungall CJ, Nehrebecky M, Robinson PN, Sincan M, Soldatos AG, Tifft CJ, Toro C, Trang H, Valkanas E, Vasilevsky N, Wahl C, Wolfe LA, Boerkoel CF, Brudno M, Haendel MA, Gahl WA, Smedley D. (2015). Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genetics in Medicine, . Link
  3. Groza, T., Kohler, S., Moldenhauer, D., Vasilevsky, N., Baynam, G., Zemojtel, T., Schriml, L. M., Kibbe, W. A., Schofield, P. N., Beck, T., et al. (2015). The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease. The American Journal of Human Genetics, . Link
  4. Haendel, M. A., Vasilevsky, N., Brush, M., Hochheiser, H. S., Jacobsen, J., Oellrich, A., Mungall, C. J., Washington, N., Kohler, S., Lewis, S. E., et al. (2015). Disease insights through cross-species phenotype comparisons. Mamm. Genome, . Link
  5. Robinson, P. N., Marco, F., Kohler, S., Notaro, M., Re, M., and Valentini, G. (2015). “A hierarchical ensemble method for DAG-structured taxonomies,” in Workshop on Multiple Classifier Systems (MCS 2015) Lecture Notes in Computer Science. (Springer International Publishing). Link
  6. Valentini, G., Kohler, S., Re, M., Notaro, M., and Robinson, P. N. (2015). “‘Prediction of Human Gene - Phenotype Associations by Exploiting the Hierarchical Structure of the Human Phenotype Ontology,’” in Bioinformatics and Biomedical Engineering Lecture Notes in Computer Science., eds. F. Ortuño and I. Rojas (Springer International Publishing), 66–77. Link
  7. Forler, S., Klein, O., Kohler, S., Robinson, P. N., Witt, H., Sultan, M., Eravci, M., Regitz-Zagrosek, V., Lehrach, H., and Klose, J. (2015). Investigation of heart proteome of different consomic mouse strains. Testing the effect of polymorphisms on the proteome-wide trans-variation of proteins. EuPA Open Proteomics 7, 27–42. Link
  8. Groza, T., Tudorache, T., Robinson, P. N., and Zankl, A. (2015). Capturing domain knowledge from multiple sources: the rare bone disorders use case. J Biomed Semantics 6, 21. Link
  9. Groza, T., Kohler, S., Doelken, S., Collier, N., Oellrich, A., Smedley, D., Couto, F. M., Baynam, G., Zankl, A., and Robinson, P. N. (2015). Automatic concept recognition using the human phenotype ontology reference and test suite corpora. Database (Oxford) 2015, . Link
  10. Deans, A. R., Lewis, S. E., Huala, E., Anzaldo, S. S., Ashburner, M., Balhoff, J. P., Blackburn, D. C., Blake, J. A., Burleigh, J. G., Chanet, B., et al. (2015). Finding our way through phenotypes. PLoS Biol. 13, e1002033. Link
  11. Ibn-Salem, J., Kohler, S., Love, M. I., Chung, H. R., Huang, N., Hurles, M. E., Haendel, M., Washington, N. L., Smedley, D., Mungall, C. J., et al. (2014). Deletions of chromosomal regulatory boundaries are associated with congenital disease. Genome Biol. 15, 423. Link
  12. Kohler, S., Schoeneberg, U., Czeschik, J. C., Doelken, S. C., Hehir-Kwa, J. Y., Ibn-Salem, J., Mungall, C. J., Smedley, D., Haendel, M. A., and Robinson, P. N. (2014). Clinical interpretation of CNVs with cross-species phenotype data. J. Med. Genet. 51, 766–772. Link
  13. Zemojtel, T., Kohler, S., Mackenroth, L., Jager, M., Hecht, J., Krawitz, P., Graul-Neumann, L., Doelken, S., Ehmke, N., Spielmann, M., et al. (2014). Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med 6, 252ra123. Link
  14. Oellrich, A., Koehler, S., Washington, N., Mungall, C., Lewis, S., Haendel, M., Robinson, P. N., and Smedley, D. (2014). The influence of disease categories on gene candidate predictions from model organism phenotypes. J Biomed Semantics 5, S4. Link
  15. Smedley, D., Kohler, S., Czeschik, J. C., Amberger, J., Bocchini, C., Hamosh, A., Veldboer, J., Zemojtel, T., and Robinson, P. N. (2014). Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases. Bioinformatics 30, 3215–3222. Link
  16. Robinson, P. N., Kohler, S., Oellrich, A., Wang, K., Mungall, C. J., Lewis, S. E., Washington, N., Bauer, S., Seelow, D., Krawitz, P., et al. (2014). Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Res. 24, 340–348. Link
  17. Kohler, S., Doelken, S. C., Mungall, C. J., Bauer, S., Firth, H. V., Bailleul-Forestier, I., Black, G. C., Brown, D. L., Brudno, M., Campbell, J., et al. (2014). The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966–974. Link
  18. Kohler, S., Doelken, S. C., Ruef, B. J., Bauer, S., Washington, N., Westerfield, M., Gkoutos, G., Schofield, P., Smedley, D., Lewis, S. E., et al. (2013). Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Res 2, 30. Link
  19. Doelken, S. C., Kohler, S., Mungall, C. J., Gkoutos, G. V., Ruef, B. J., Smith, C., Smedley, D., Bauer, S., Klopocki, E., Schofield, P. N., et al. (2013). Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish. Dis Model Mech 6, 358–372. Link
  20. Bauer, S., Kohler, S., Schulz, M. H., and Robinson, P. N. (2012). Bayesian ontology querying for accurate and noise-tolerant semantic searches. Bioinformatics 28, 2502–2508. Link
  21. Kohler, S., Doelken, S. C., Rath, A., Ayme, S., and Robinson, P. N. (2012). Ontological phenotype standards for neurogenetics. Hum. Mutat. 33, 1333–1339. Link
  22. Chen, C. K., Mungall, C. J., Gkoutos, G. V., Doelken, S. C., Kohler, S., Ruef, B. J., Smith, C., Westerfield, M., Robinson, P. N., Lewis, S. E., et al. (2012). MouseFinder: Candidate disease genes from mouse phenotype data. Hum. Mutat. 33, 858–866. Link
  23. Kohler, S., Bauer, S., Mungall, C. J., Carletti, G., Smith, C. L., Schofield, P., Gkoutos, G. V., and Robinson, P. N. (2011). Improving ontologies by automatic reasoning and evaluation of logical definitions. BMC Bioinformatics 12, 418. Link
  24. Schulz, M. H., Kohler, S., Bauer, S., and Robinson, P. N. (2011). Exact score distribution computation for ontological similarity searches. BMC Bioinformatics 12, 441. Link
  25. Kohler, S., Schulz, M. H., Krawitz, P., Bauer, S., Dolken, S., Ott, C. E., Mundlos, C., Horn, D., Mundlos, S., and Robinson, P. N. (2009). Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am. J. Hum. Genet. 85, 457–464. Link
  26. Gkoutos, G. V., Mungall, C., Dolken, S., Ashburner, M., Lewis, S., Hancock, J., Schofield, P., Kohler, S., and Robinson, P. N. (2009). Entity/quality-based logical definitions for the human skeletal phenome using PATO. Conf Proc IEEE Eng Med Biol Soc 2009, 7069–7072. Link
  27. Robinson, P. N., Kohler, S., Bauer, S., Seelow, D., Horn, D., and Mundlos, S. (2008). The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615. Link
  28. Kohler, S., Bauer, S., Horn, D., and Robinson, P. N. (2008). Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82, 949–958. Link
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