| Year |
Citation |
Score |
| 2023 |
Muzio G, O'Bray L, Meng-Papaxanthos L, Klatt J, Fischer K, Borgwardt K. networkGWAS: a network-based approach to discover genetic associations. Bioinformatics (Oxford, England). 39. PMID 37285313 DOI: 10.1093/bioinformatics/btad370 |
0.306 |
|
| 2020 |
Höllerer S, Papaxanthos L, Gumpinger AC, Fischer K, Beisel C, Borgwardt K, Benenson Y, Jeschek M. Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nature Communications. 11: 3551. PMID 32669542 DOI: 10.1038/S41467-020-17222-4 |
0.341 |
|
| 2020 |
Gumpinger AC, Rieck B, Grimm DG, Borgwardt K. Network-guided search for genetic heterogeneity between gene pairs. Bioinformatics (Oxford, England). PMID 32573681 DOI: 10.1093/Bioinformatics/Btaa581 |
0.376 |
|
| 2019 |
Togninalli M, Seren Ü, Freudenthal JA, Monroe JG, Meng D, Nordborg M, Weigel D, Borgwardt K, Korte A, Grimm DG. AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana. Nucleic Acids Research. PMID 31642487 DOI: 10.1093/Nar/Gkz925 |
0.581 |
|
| 2018 |
Gumpinger AC, Roqueiro D, Grimm DG, Borgwardt KM. Methods and Tools in Genome-wide Association Studies. Methods in Molecular Biology (Clifton, N.J.). 1819: 93-136. PMID 30421401 DOI: 10.1007/978-1-4939-8618-7_5 |
0.369 |
|
| 2017 |
Togninalli M, Seren Ü, Meng D, Fitz J, Nordborg M, Weigel D, Borgwardt K, Korte A, Grimm DG. The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. Nucleic Acids Research. PMID 29059333 DOI: 10.1093/Nar/Gkx954 |
0.56 |
|
| 2017 |
Llinares-López F, Papaxanthos L, Bodenham D, Roqueiro D, Investigators C, Borgwardt K. Genome-wide genetic heterogeneity discovery with categorical covariates. Bioinformatics (Oxford, England). PMID 28200033 DOI: 10.1093/bioinformatics/btx071 |
0.332 |
|
| 2016 |
Grimm DG, Roqueiro D, Salome P, Kleeberger S, Greshake B, Zhu W, Liu C, Lippert C, Stegle O, Schölkopf B, Weigel D, Borgwardt K. easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies. The Plant Cell. PMID 27986896 DOI: 10.1105/Tpc.16.00551 |
0.505 |
|
| 2016 |
Seren Ü, Grimm D, Fitz J, Weigel D, Nordborg M, Borgwardt K, Korte A. AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Research. PMID 27924043 DOI: 10.1093/Nar/Gkw986 |
0.531 |
|
| 2016 |
Seymour DK, Chae E, Grimm DG, Martín Pizarro C, Habring-Müller A, Vasseur F, Rakitsch B, Borgwardt KM, Koenig D, Weigel D. Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids. Proceedings of the National Academy of Sciences of the United States of America. PMID 27803326 DOI: 10.1073/Pnas.1615268113 |
0.491 |
|
| 2016 |
McGaughran A, Rödelsperger C, Grimm DG, Meyer JM, Moreno E, Morgan K, Leaver M, Serobyan V, Rakitsch B, Borgwardt KM, Sommer RJ. Genomic profiles of diversification and genotype-phenotype association in island nematode lineages. Molecular Biology and Evolution. PMID 27189551 DOI: 10.1093/Molbev/Msw093 |
0.355 |
|
| 2015 |
Llinares-López F, Grimm DG, Bodenham DA, Gieraths U, Sugiyama M, Rowan B, Borgwardt K. Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinformatics (Oxford, England). 31: i240-i249. PMID 26072488 DOI: 10.1093/Bioinformatics/Btv263 |
0.436 |
|
| 2015 |
Hagmann J, Becker C, Müller J, Stegle O, Meyer RC, Wang G, Schneeberger K, Fitz J, Altmann T, Bergelson J, Borgwardt K, Weigel D. Century-scale methylome stability in a recently diverged Arabidopsis thaliana lineage. Plos Genetics. 11: e1004920. PMID 25569172 DOI: 10.1371/Journal.Pgen.1004920 |
0.55 |
|
| 2015 |
Hagmann J, Becker C, Müller J, Stegle O, Meyer RC, Wang G, Schneeberger K, Fitz J, Altmann T, Bergelson J, Borgwardt K, Weigel D. Epigenetic variation in a nearly isogenic population. Plos Genetics. DOI: 10.1371/Journal.Pgen.1004920.G002 |
0.445 |
|
| 2013 |
Azencott CA, Grimm D, Sugiyama M, Kawahara Y, Borgwardt KM. Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics (Oxford, England). 29: i171-9. PMID 23812981 DOI: 10.1093/Bioinformatics/Btt238 |
0.307 |
|
| 2013 |
Drewe P, Stegle O, Hartmann L, Kahles A, Bohnert R, Wachter A, Borgwardt K, Rätsch G. Accurate detection of differential RNA processing. Nucleic Acids Research. 41: 5189-98. PMID 23585274 DOI: 10.1093/Nar/Gkt211 |
0.305 |
|
| 2013 |
Fusi N, Lippert C, Borgwardt K, Lawrence ND, Stegle O. Detecting regulatory gene-environment interactions with unmeasured environmental factors. Bioinformatics (Oxford, England). 29: 1382-9. PMID 23559640 DOI: 10.1093/Bioinformatics/Btt148 |
0.375 |
|
| 2013 |
Grimm D, Hagmann J, Koenig D, Weigel D, Borgwardt K. Accurate indel prediction using paired-end short reads. Bmc Genomics. 14: 132. PMID 23442375 DOI: 10.1186/1471-2164-14-132 |
0.487 |
|
| 2013 |
Rakitsch B, Lippert C, Stegle O, Borgwardt K. A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics (Oxford, England). 29: 206-14. PMID 23175758 DOI: 10.1093/Bioinformatics/Bts669 |
0.404 |
|
| 2011 |
Becker C, Hagmann J, Müller J, Koenig D, Stegle O, Borgwardt K, Weigel D. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature. 480: 245-9. PMID 22057020 DOI: 10.1038/Nature10555 |
0.442 |
|
| 2011 |
Cao J, Schneeberger K, Ossowski S, Günther T, Bender S, Fitz J, Koenig D, Lanz C, Stegle O, Lippert C, Wang X, Ott F, Müller J, Alonso-Blanco C, Borgwardt K, et al. Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nature Genetics. 43: 956-63. PMID 21874002 DOI: 10.1038/Ng.911 |
0.488 |
|
| 2011 |
Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K. Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinformatics (Oxford, England). 27: i214-21. PMID 21685073 DOI: 10.1093/bioinformatics/btr218 |
0.322 |
|
| 2011 |
Grimm DG, Hagmann J, Koenig D, Weigel D, Borgwardt K. Support vector machines for finding deletions and short insertions using paired-end short reads F1000research. 2. DOI: 10.7490/F1000Research.2126.1 |
0.409 |
|
| 2011 |
Klotzbücher K, Kobayashi Y, Shervashidze N, Stegle O, Müller-Myhsok B, Weigel D, Borgwardt K. Efficient branch-and-bound techniques for two-locus association mapping Bmc Bioinformatics. 12. DOI: 10.1186/1471-2105-12-S11-A3 |
0.414 |
|
| 2010 |
Lippert C, Stegle O, Nickisch H, Borgwardt K, Weigel D. Experimental design for genome-wide association studies F1000research. 1. DOI: 10.7490/F1000Research.324.1 |
0.488 |
|
| 2010 |
Stegle O, Drewe P, Bohnert R, Borgwardt K, Rätsch G. Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts Nature Precedings. 2010: 1-11. DOI: 10.1038/Npre.2010.4437.1 |
0.306 |
|
| 2008 |
Pain A, Böhme U, Berry AE, Mungall K, Finn RD, Jackson AP, Mourier T, Mistry J, Pasini EM, Aslett MA, Balasubrammaniam S, Borgwardt K, Brooks K, Carret C, Carver TJ, et al. The genome of the simian and human malaria parasite Plasmodium knowlesi. Nature. 455: 799-803. PMID 18843368 DOI: 10.1038/Nature07306 |
0.325 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2018 |
Llinares-López F, Papaxanthos L, Roqueiro D, Bodenham D, Borgwardt K. CASMAP: Detection of statistically significant combinations of SNPs in association mapping. Bioinformatics (Oxford, England). PMID 30541062 DOI: 10.1093/bioinformatics/bty1020 |
0.299 |
|
| 2012 |
Kam-Thong T, Azencott CA, Cayton L, Pütz B, Altmann A, Karbalai N, Sämann PG, Schölkopf B, Müller-Myhsok B, Borgwardt KM. GLIDE: GPU-based linear regression for detection of epistasis. Human Heredity. 73: 220-36. PMID 22965145 DOI: 10.1159/000341885 |
0.282 |
|
| 2023 |
Pellizzoni P, Muzio G, Borgwardt K. Higher-order genetic interaction discovery with network-based biological priors. Bioinformatics (Oxford, England). 39: i523-i533. PMID 37387173 DOI: 10.1093/bioinformatics/btad273 |
0.28 |
|
| 2020 |
Gumpinger AC, Lage K, Horn H, Borgwardt K. Prediction of cancer driver genes through network-based moment propagation of mutation scores. Bioinformatics (Oxford, England). 36: i508-i515. PMID 32657361 DOI: 10.1093/Bioinformatics/Btaa452 |
0.276 |
|
| 2018 |
Togninalli M, Roqueiro D, Borgwardt KM. Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts. Bioinformatics (Oxford, England). 34: i687-i696. PMID 30423082 DOI: 10.1093/bioinformatics/bty596 |
0.272 |
|
| 2012 |
Karaletsos T, Stegle O, Dreyer C, Winn J, Borgwardt KM. ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinformatics (Oxford, England). 28: 1001-8. PMID 22333244 DOI: 10.1093/bioinformatics/bts081 |
0.269 |
|
| 2020 |
Hyland SL, Faltys M, Hüser M, Lyu X, Gumbsch T, Esteban C, Bock C, Horn M, Moor M, Rieck B, Zimmermann M, Bodenham D, Borgwardt K, Rätsch G, Merz TM. Early prediction of circulatory failure in the intensive care unit using machine learning. Nature Medicine. 26: 364-373. PMID 32152583 DOI: 10.1038/S41591-020-0789-4 |
0.263 |
|
| 2020 |
Weis C, Horn M, Rieck B, Cuénod A, Egli A, Borgwardt K. Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Bioinformatics (Oxford, England). 36: i30-i38. PMID 32657381 DOI: 10.1093/Bioinformatics/Btaa429 |
0.258 |
|
| 2018 |
He X, Folkman L, Borgwardt K. Kernelized rank learning for personalized drug recommendation. Bioinformatics (Oxford, England). PMID 29528376 DOI: 10.7490/F1000Research.1114808.1 |
0.257 |
|
| 2021 |
Bock C, Moor M, Jutzeler CR, Borgwardt K. Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning. Methods in Molecular Biology (Clifton, N.J.). 2190: 33-71. PMID 32804360 DOI: 10.1007/978-1-0716-0826-5_2 |
0.248 |
|
| 2018 |
Gärtner M, Ghisu ME, Scheidegger M, Bönke L, Fan Y, Stippl A, Herrera-Melendez AL, Metz S, Winnebeck E, Fissler M, Henning A, Bajbouj M, Borgwardt K, Barnhofer T, Grimm S. Aberrant working memory processing in major depression: evidence from multivoxel pattern classification. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology. PMID 29777198 DOI: 10.1038/S41386-018-0081-1 |
0.238 |
|
| 2023 |
Togninalli M, Wang X, Kucera T, Shrestha S, Juliana P, Mondal S, Pinto F, Govindan V, Crespo-Herrera L, Huerta-Espino J, Singh RP, Borgwardt K, Poland J. Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics. Bioinformatics (Oxford, England). PMID 37220903 DOI: 10.1093/bioinformatics/btad336 |
0.224 |
|
| 2019 |
He X, Gumbsch T, Roqueiro D, Borgwardt K. Kernel conditional clustering and kernel conditional semi-supervised learning Knowledge and Information Systems. 62: 899-925. DOI: 10.1007/S10115-019-01334-5 |
0.221 |
|
| 2015 |
Roqueiro D, Witteveen MJ, Anttila V, Terwindt GM, van den Maagdenberg AM, Borgwardt K. In silico phenotyping via co-training for improved phenotype prediction from genotype. Bioinformatics (Oxford, England). 31: i303-10. PMID 26072497 DOI: 10.1093/bioinformatics/btv254 |
0.22 |
|
| 2018 |
Bock C, Gumbsch T, Moor M, Rieck B, Roqueiro D, Borgwardt K. Association mapping in biomedical time series via statistically significant shapelet mining. Bioinformatics (Oxford, England). 34: i438-i446. PMID 29949972 DOI: 10.1093/bioinformatics/bty246 |
0.213 |
|
| 2015 |
Grimm DG, Azencott CA, Aicheler F, Gieraths U, MacArthur DG, Samocha KE, Cooper DN, Stenson PD, Daly MJ, Smoller JW, Duncan LE, Borgwardt KM. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human Mutation. 36: 513-23. PMID 25684150 DOI: 10.1002/Humu.22768 |
0.213 |
|
| 2020 |
Jutzeler CR, Bourguignon L, Weis CV, Tong B, Wong C, Rieck B, Pargger H, Tschudin-Sutter S, Egli A, Borgwardt K, Walter M. Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis. Travel Medicine and Infectious Disease. 101825. PMID 32763496 DOI: 10.1016/J.Tmaid.2020.101825 |
0.209 |
|
| 2011 |
Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, Wolf C, Ziegler A, Pütz B, Holsboer F, Schölkopf B, et al. EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. European Journal of Human Genetics : Ejhg. 19: 465-71. PMID 21150885 DOI: 10.1038/Ejhg.2010.196 |
0.208 |
|
| 2019 |
Togninalli M, Yoneoka D, Kolios AGA, Borgwardt K, Nilsson J. Pretransplant Kinetics of Anti-HLA Antibodies in Patients on the Waiting List for Kidney Transplantation. Journal of the American Society of Nephrology : Jasn. 30: 2262-2274. PMID 31653784 DOI: 10.1681/Asn.2019060594 |
0.203 |
|
| 2010 |
Shervashidze N, Smola A, Borgwardt K. Scalable graph kernels with approximate matching of subtree patterns F1000research. 1. DOI: 10.7490/F1000Research.331.1 |
0.195 |
|
| 2023 |
Adamer MF, Roellin E, Bourguignon L, Borgwardt K. SIMBSIG: similarity search and clustering for biobank-scale data. Bioinformatics (Oxford, England). 39. PMID 36610707 DOI: 10.1093/bioinformatics/btac829 |
0.185 |
|
| 2023 |
Adamer MF, Roellin E, Bourguignon L, Borgwardt K. SIMBSIG: similarity search and clustering for biobank-scale data. Bioinformatics (Oxford, England). 39. PMID 36610707 DOI: 10.1093/bioinformatics/btac829 |
0.185 |
|
| 2023 |
Adamer MF, Roellin E, Bourguignon L, Borgwardt K. SIMBSIG: similarity search and clustering for biobank-scale data. Bioinformatics (Oxford, England). 39. PMID 36610707 DOI: 10.1093/bioinformatics/btac829 |
0.185 |
|
| 2011 |
Li L, Rakitsch B, Borgwardt K. ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinformatics (Oxford, England). 27: i342-8. PMID 21685091 DOI: 10.1093/bioinformatics/btr204 |
0.18 |
|
| 2008 |
Borgwardt K. Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model. Bioessays : News and Reviews in Molecular, Cellular and Developmental Biology. 30: 707-10. PMID 18618771 DOI: 10.1002/bies.20783 |
0.178 |
|
| 2022 |
Adamer MF, Brüningk SC, Tejada-Arranz A, Estermann F, Basler M, Borgwardt K. reComBat: batch-effect removal in large-scale multi-source gene-expression data integration. Bioinformatics Advances. 2: vbac071. PMID 36699372 DOI: 10.1093/bioadv/vbac071 |
0.172 |
|
| 2012 |
Windram O, Madhou P, McHattie S, Hill C, Hickman R, Cooke E, Jenkins DJ, Penfold CA, Baxter L, Breeze E, Kiddle SJ, Rhodes J, Atwell S, Kliebenstein DJ, Kim YS, ... ... Borgwardt K, et al. Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. The Plant Cell. 24: 3530-57. PMID 23023172 DOI: 10.1105/Tpc.112.102046 |
0.17 |
|
| 2018 |
Li L, He X, Borgwardt K. Multi-target drug repositioning by bipartite block-wise sparse multi-task learning. Bmc Systems Biology. 12: 55. PMID 29745839 DOI: 10.1186/s12918-018-0569-7 |
0.162 |
|
| 2006 |
Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Schölkopf B, Smola AJ. Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics (Oxford, England). 22: e49-57. PMID 16873512 DOI: 10.1093/Bioinformatics/Btl242 |
0.159 |
|
| 2021 |
Moor M, Rieck B, Horn M, Jutzeler CR, Borgwardt K. Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine. 8: 607952. PMID 34124082 DOI: 10.3389/fmed.2021.607952 |
0.152 |
|
| 2014 |
Sugiyama M, Azencott CA, Grimm D, Kawahara Y, Borgwardt KM. Multi-task feature selection on multiple networks via maximum flows Siam International Conference On Data Mining 2014, Sdm 2014. 1: 199-207. DOI: 10.1137/1.9781611973440.23 |
0.15 |
|
| 2007 |
Song L, Bedo J, Borgwardt KM, Gretton A, Smola A. Gene selection via the BAHSIC family of algorithms. Bioinformatics (Oxford, England). 23: i490-8. PMID 17646335 DOI: 10.1093/bioinformatics/btm216 |
0.15 |
|
| 2010 |
Lippert C, Ghahramani Z, Borgwardt KM. Gene function prediction from synthetic lethality networks via ranking on demand. Bioinformatics (Oxford, England). 26: 912-8. PMID 20154010 DOI: 10.1093/bioinformatics/btq053 |
0.145 |
|
| 2024 |
Hornauer P, Prack G, Anastasi N, Ronchi S, Kim T, Donner C, Fiscella M, Borgwardt K, Taylor V, Jagasia R, Roqueiro D, Hierlemann A, Schröter M. DeePhys: A machine learning-assisted platform for electrophysiological phenotyping of human neuronal networks. Stem Cell Reports. PMID 38278155 DOI: 10.1016/j.stemcr.2023.12.008 |
0.145 |
|
| 2020 |
Gumbsch T, Bock C, Moor M, Rieck B, Borgwardt K. Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. Bioinformatics (Oxford, England). 36: i840-i848. PMID 33381811 DOI: 10.1093/bioinformatics/btaa815 |
0.142 |
|
| 2010 |
Stegle O, Denby KJ, Cooke EJ, Wild DL, Ghahramani Z, Borgwardt KM. A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 17: 355-67. PMID 20377450 DOI: 10.1089/Cmb.2009.0175 |
0.141 |
|
| 2020 |
Gärtner M, Ghisu E, Herrera-Melendez AL, Koslowski M, Aust S, Asbach P, Otte C, Regen F, Heuser I, Borgwardt K, Grimm S, Bajbouj M. Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy. Experimental Neurology. 335: 113505. PMID 33068570 DOI: 10.1016/j.expneurol.2020.113505 |
0.133 |
|
| 2006 |
Borgwardt KM, Vishwanathan SV, Kriegel HP. Class prediction from time series gene expression profiles using dynamical systems kernels. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 547-58. PMID 17094268 |
0.132 |
|
| 2020 |
Muzio G, O'Bray L, Borgwardt K. Biological network analysis with deep learning. Briefings in Bioinformatics. PMID 33169146 DOI: 10.1093/bib/bbaa257 |
0.131 |
|
| 2021 |
Born J, Wiedemann N, Cossio M, Buhre C, Brändle G, Leidermann K, Aujayeb A, Moor M, Rieck B, Borgwardt K. Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis Applied Sciences. 11: 672. DOI: 10.3390/app11020672 |
0.126 |
|
| 2022 |
Brüningk SC, Klatt J, Stange M, Mari A, Brunner M, Roloff TC, Seth-Smith HMB, Schweitzer M, Leuzinger K, Søgaard KK, Albertos Torres D, Gensch A, Schlotterbeck AK, Nickel CH, Ritz N, ... ... Borgwardt KM, et al. Determinants of SARS-CoV-2 transmission to guide vaccination strategy in an urban area. Virus Evolution. 8: veac002. PMID 35310621 DOI: 10.1093/ve/veac002 |
0.123 |
|
| 2022 |
Fan B, Klatt J, Moor MM, Daniels LA, Sanchez-Pinto LN, Agyeman PKA, Schlapbach LJ, Borgwardt KM. Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients. Bioinformatics (Oxford, England). 38: i101-i108. PMID 35758775 DOI: 10.1093/bioinformatics/btac229 |
0.12 |
|
| 2011 |
Achlioptas P, Schölkopf B, Borgwardt KM. Two-locus association mapping in subquadratic time Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 726-734. DOI: 10.1145/2020408.2020521 |
0.114 |
|
| 2020 |
Egli A, Battegay M, Büchler AC, Bühlmann P, Calandra T, Eckert P, Furrer H, Greub G, Jakob SM, Kaiser L, Leib SL, Marsch S, Meinshausen N, Pagani JL, Pugin J, ... ... Borgwardt K, et al. SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. Studies in Health Technology and Informatics. 270: 1163-1167. PMID 32570564 DOI: 10.3233/SHTI200346 |
0.111 |
|
| 2023 |
Moor M, Bennett N, Plečko D, Horn M, Rieck B, Meinshausen N, Bühlmann P, Borgwardt K. Predicting sepsis using deep learning across international sites: a retrospective development and validation study. Eclinicalmedicine. 62: 102124. PMID 37588623 DOI: 10.1016/j.eclinm.2023.102124 |
0.11 |
|
| 2015 |
Llinares-López F, Sugiyama M, Papaxanthos L, Borgwardt KM. Fast and memory-efficient significant pattern mining via permutation testing Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 2015: 725-734. DOI: 10.1145/2783258.2783363 |
0.11 |
|
| 2013 |
Feragen A, Petersen J, Grimm D, Dirksen A, Pedersen JH, Borgwardt K, de Bruijne M. Geometric tree kernels: classification of COPD from airway tree geometry. Information Processing in Medical Imaging : Proceedings of the ... Conference. 23: 171-83. PMID 24683967 DOI: 10.1007/978-3-642-38868-2_15 |
0.108 |
|
| 2007 |
Borgwardt KM, Kriegel HP, Vishwanathan SV, Schraudolph NN. Graph kernels for disease outcome prediction from protein-protein interaction networks. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 4-15. PMID 17992741 |
0.107 |
|
| 2023 |
Visonà G, Duroux D, Miranda L, Sükei E, Li Y, Borgwardt K, Oliver C. Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. Bioinformatics (Oxford, England). PMID 38001023 DOI: 10.1093/bioinformatics/btad717 |
0.106 |
|
| 2007 |
Kriegel H, Borgwardt KM, Kröger P, Pryakhin A, Schubert M, Zimek A. Future trends in data mining Data Mining and Knowledge Discovery. 15: 87-97. DOI: 10.1007/s10618-007-0067-9 |
0.104 |
|
| 2022 |
Weis C, Cuénod A, Rieck B, Dubuis O, Graf S, Lang C, Oberle M, Brackmann M, Søgaard KK, Osthoff M, Borgwardt K, Egli A. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nature Medicine. 28: 164-174. PMID 35013613 DOI: 10.1038/s41591-021-01619-9 |
0.103 |
|
| 2021 |
Avican K, Aldahdooh J, Togninalli M, Mahmud AKMF, Tang J, Borgwardt KM, Rhen M, Fällman M. RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection. Nature Communications. 12: 3282. PMID 34078900 DOI: 10.1038/s41467-021-23588-w |
0.091 |
|
| 2024 |
Cervia-Hasler C, Brüningk SC, Hoch T, Fan B, Muzio G, Thompson RC, Ceglarek L, Meledin R, Westermann P, Emmenegger M, Taeschler P, Zurbuchen Y, Pons M, Menges D, Ballouz T, ... ... Borgwardt K, et al. Persistent complement dysregulation with signs of thromboinflammation in active Long Covid. Science (New York, N.Y.). 383: eadg7942. PMID 38236961 DOI: 10.1126/science.adg7942 |
0.086 |
|
| 2005 |
Borgwardt KM, Ong CS, Schönauer S, Vishwanathan SV, Smola AJ, Kriegel HP. Protein function prediction via graph kernels. Bioinformatics (Oxford, England). 21: i47-56. PMID 15961493 DOI: 10.1093/Bioinformatics/Bti1007 |
0.085 |
|
| 2006 |
Vishwanathan SVN, Borgwardt KM, Guttman O, Smola A. Kernel extrapolation Neurocomputing. 69: 721-729. DOI: 10.1016/J.Neucom.2005.12.113 |
0.082 |
|
| 2021 |
Gumbsch T, Borgwardt K. Ethnicity-based bias in clinical severity scores. The Lancet. Digital Health. 3: e209-e210. PMID 33766286 DOI: 10.1016/S2589-7500(21)00044-3 |
0.068 |
|
| 2017 |
Sugiyama M, Ghisu ME, Llinares-López F, Borgwardt K. graphkernels: R and Python packages for graph comparison. Bioinformatics (Oxford, England). PMID 29028902 DOI: 10.1093/bioinformatics/btx602 |
0.064 |
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| 2020 |
Borgwardt K, Ghisu E, Llinares-López F, O’Bray L, Rieck B. Graph Kernels: State-of-the-Art and Future Challenges Foundations and Trends® in Machine Learning. 13: 531-712. DOI: 10.1561/2200000076 |
0.055 |
|
| 2010 |
Thoma M, Cheng H, Gretton A, Han J, Kriegel H, Smola A, Song L, Yu PS, Yan X, Borgwardt KM. Discriminative frequent subgraph mining with optimality guarantees Statistical Analysis and Data Mining. 3: 302-318. DOI: 10.1002/sam.10084 |
0.048 |
|
| 2019 |
Borgwardt K, Loh P, Terzi E, Ukkonen A. Introduction to the special issue for the ECML PKDD 2019 journal track Machine Learning. 108: 1191-1192. DOI: 10.1007/s10994-019-05831-0 |
0.047 |
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| 2019 |
Borgwardt K, Loh P, Terzi E, Ukkonen A. Introduction to the special issue for the ECML PKDD 2019 journal track Data Mining and Knowledge Discovery. 33: 1223-1224. DOI: 10.1007/s10618-019-00642-2 |
0.047 |
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| 2010 |
Camps-Valls G, Shervashidze N, Borgwardt KM. Spatio-Spectral Remote Sensing Image Classification With Graph Kernels Ieee Geoscience and Remote Sensing Letters. 7: 741-745. DOI: 10.1109/LGRS.2010.2046618 |
0.042 |
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| 2009 |
Kondor R, Shervashidze N, Borgwardt KM. The graphlet spectrum Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 529-536. DOI: 10.1145/1553374.1553443 |
0.025 |
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| 2008 |
Hübler C, Kriegel HP, Borgwardt K, Ghahramani Z. Metropolis algorithms for representative subgraph sampling Proceedings - Ieee International Conference On Data Mining, Icdm. 283-292. DOI: 10.1109/ICDM.2008.124 |
0.024 |
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