Karsten Borgwardt

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH在 tuebingen.mpg.de 的電子郵件地址已通過驗證
Bastian RieckProfessor, AIDOS Lab, University of Fribourg在 unifr.ch 的電子郵件地址已通過驗證
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Oliver StegleGerman Cancer Research Center (DKFZ) & EMBL Heidelberg在 dkfz.de 的電子郵件地址已通過驗證
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue University在 stat.purdue.edu 的電子郵件地址已通過驗證
Detlef WeigelMax Planck Institute for Biology Tübingen在 weigelworld.org 的電子郵件地址已通過驗證
Dominik G. GrimmProfessor, TUM Campus Straubing, HSWT在 hswt.de 的電子郵件地址已通過驗證
Christoph LippertProfessor, Hasso Plattner Institute, Universität Potsdam在 hpi.de 的電子郵件地址已通過驗證
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Le SongGenBio AI, Mohamed bin Zayed University of Artificial Intelligence在 mbzuai.ac.ae 的電子郵件地址已通過驗證
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Barbara RakitschBosch Center for AI在 de.bosch.com 的電子郵件地址已通過驗證
Aasa FeragenProfessor, DTU Compute在 dtu.dk 的電子郵件地址已通過驗證
Cheng Soon OngData61, CSIRO, Canberra在 anu.edu.au 的電子郵件地址已通過驗證

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Karsten Borgwardt

Director, Max Planck Institute of Biochemistry

在 biochem.mpg.de 的電子郵件地址已通過驗證 - 首頁

Machine Learning Systems Biology Bioinformatics Computational Biology Data Mining


標題按引用次數排序按年份排序按標題排序	引用次數引用次數	年份
A kernel two-sample test A Gretton, KM Borgwardt, MJ Rasch, B Schölkopf, A Smola The Journal of Machine Learning Research 13 (1), 723-773, 2012	6458	2012
A kernel method for the two-sample-problem A Gretton, K Borgwardt, M Rasch, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2903	2006
Weisfeiler-lehman graph kernels. N Shervashidze, P Schweitzer, EJ Van Leeuwen, K Mehlhorn, ... Journal of Machine Learning Research 12 (9), 2011	2723	2011
Correcting sample selection bias by unlabeled data J Huang, A Gretton, K Borgwardt, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2285	2006
Integrating structured biological data by kernel maximum mean discrepancy KM Borgwardt, A Gretton, MJ Rasch, HP Kriegel, B Schölkopf, AJ Smola Bioinformatics 22 (14), e49-e57, 2006	1849	2006
Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt The Journal of Machine Learning Research 11, 1201-1242, 2010	1517	2010
Protein function prediction via graph kernels KM Borgwardt, CS Ong, S Schönauer, SVN Vishwanathan, AJ Smola, ... Bioinformatics 21 (suppl_1), i47-i56, 2005	1470	2005
Shortest-path kernels on graphs KM Borgwardt, HP Kriegel Fifth IEEE international conference on data mining (ICDM'05), 8 pp., 2005	1398	2005
Efficient graphlet kernels for large graph comparison N Shervashidze, SVN Vishwanathan, T Petri, K Mehlhorn, K Borgwardt Artificial intelligence and statistics, 488-495, 2009	1361	2009
1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana C Alonso-Blanco, J Andrade, C Becker, F Bemm, J Bergelson, ... Cell 166 (2), 481-491, 2016	1292	2016
Whole-genome sequencing of multiple Arabidopsis thaliana populations J Cao, K Schneeberger, S Ossowski, T Günther, S Bender, J Fitz, ... Nature genetics 43 (10), 956-963, 2011	1114	2011
Covariate shift by kernel mean matching A Gretton, A Smola, J Huang, M Schmittfull, K Borgwardt, B Schölkopf Dataset shift in machine learning 3 (4), 5, 2009	862	2009
Spontaneous epigenetic variation in the Arabidopsis thaliana methylome C Becker, J Hagmann, J Müller, D Koenig, O Stegle, K Borgwardt, ... Nature 480 (7376), 245-249, 2011	793	2011
Epigenomic diversity in a global collection of Arabidopsis thaliana accessions T Kawakatsu, SC Huang, F Jupe, E Sasaki, RJ Schmitz, MA Urich, ... Cell 166 (2), 492-505, 2016	697	2016
Feature selection via dependence maximization L Song, A Smola, A Gretton, J Bedo, K Borgwardt The Journal of Machine Learning Research 13, 1393-1434, 2012	525	2012
The genome of the simian and human malaria parasite Plasmodium knowlesi A Pain, U Böhme, AE Berry, K Mungall, RD Finn, AP Jackson, T Mourier, ... Nature 455 (7214), 799-803, 2008	491	2008
Supervised feature selection via dependence estimation L Song, A Smola, A Gretton, KM Borgwardt, J Bedo Proceedings of the 24th international conference on Machine learning, 823-830, 2007	482	2007
Early prediction of circulatory failure in the intensive care unit using machine learning SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ... Nature medicine 26 (3), 364-373, 2020	414	2020
The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity DG Grimm, CA Azencott, F Aicheler, U Gieraths, DG MacArthur, ... Human mutation 36 (5), 513-523, 2015	412	2015
Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis O Windram, P Madhou, S McHattie, C Hill, R Hickman, E Cooke, ... The Plant Cell 24 (9), 3530-3557, 2012	399	2012

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