Borgwardt, Karsten

A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007) · Advances in Neural Information Processing Systems 19

Probability and Statistical ResearchStatistical Methods and Applications

Cited by 2,167

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics

Gene expression and cancer classificationBioinformatics and Genomic Networks

Cited by 1,674

Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland, S. L., Faltys, M., Hüser, M., Lyu, X., Gumbsch, T., Esteban, C., Bock, C., Horn, M., Moor, M., Rieck, B., et al. (2020) · Nature Medicine

Machine Learning in HealthcareHealthcare Technology and Patient Monitoring

Cited by 477

The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity

Grimm, D. G., Azencott, C., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., Cooper, D. N., Stenson, P. D., Daly, M. J., Smoller, J. W., et al. (2015) · Human Mutation

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 356

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., Bühlmann, P., & Borgwardt, K. (2023) · eClinicalMedicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 118

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Togninalli, M., Wang, X., Kucera, T., Shrestha, S., Juliana, P., Mondal, S., Pinto, F., Govindan, V., Crespo-Herrera, L., Huerta-Espino, J., et al. (2023) · Bioinformatics

Smart Agriculture and AIGenetics and Plant Breeding

Cited by 49

Aberrant working memory processing in major depression: evidence from multivoxel pattern classification

Gärtner, M., Ghisu, M. E., Scheidegger, M., Bönke, L., Fan, Y., Stippl, A., Herrera-Melendez, A.-L., Metz, S., Winnebeck, E., Fissler, M., et al. (2018) · Neuropsychopharmacology

Neural and Behavioral Psychology StudiesTreatment of Major Depression

Cited by 41

Topological Graph Neural Networks

Horn, M., De Brouwer, E., Moor, M., Moreau, Y., Rieck, B., & Borgwardt, K. (2021) · arXiv

Advanced Graph Neural NetworksTopological and Geometric Data Analysis

Cited by 31

reComBat: batch-effect removal in large-scale multi-source gene-expression data integration

Adamer, M. F., Brüningk, S. C., Tejada-Arranz, A., Estermann, F., Basler, M., & Borgwardt, K. (2022) · Bioinformatics Advances

Gene expression and cancer classificationSingle-cell and spatial transcriptomics

Cited by 27