Dmitry Kobak

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Philipp BerensHertie Institute for AI in Brain Health, University of TübingenVerified email at uni-tuebingen.de
Andreas ToliasDepartment of Neuroscience, Baylor College of MedicineVerified email at cns.bcm.edu
Yves BernaertsPostdoc, Champalimaud FoundationVerified email at research.fchampalimaud.org
Cathryn CadwellAssistant Professor, University of California San FranciscoVerified email at ucsf.edu
Xiaolong JiangBaylor College of MedicineVerified email at bcm.edu
Christian MachensChampalimaud Neuroscience Programme, Champalimaud FoundationVerified email at neuro.fchampalimaud.org
Zheng Huan TanBaylor College of MedicineVerified email at bcm.edu
Rickard SandbergProfessor, Karolinska InstitutetVerified email at ki.se
Leonard HartmanisPhD in single-cell genomicsVerified email at ki.se
Sophie LaturnusCentre of Integrative Neuroscience, University of TübingenVerified email at uni-tuebingen.de
Sergey ShpilkinVerified email at freemoscow.university
Naoshige UchidaProfessor of Molecular and Cellular Biology, Harvard UniversityVerified email at mcb.harvard.edu
Jesus Ramon CastroPhD Student, University of MiamiVerified email at miami.edu
Maxim S. PshenichnikovUniversity of GroningenVerified email at rug.nl
Christos ConstantinidisStevenson Chair of Biomedical Engineering, Vanderbilt UniversityVerified email at vanderbilt.edu
George C. LindermanMassachusetts General HospitalVerified email at mgh.harvard.edu
Ariel KarlinskyHebrew UniversityVerified email at mail.huji.ac.il
Zachary F MainenChampalimaud Neuroscience ProgrammeVerified email at neuro.fchampalimaud.org
ranulfo romoProfesor de Neurociencias, Universidad Nacional Autónoma de MéxicoVerified email at ifc.unam.mx
Wieland BrendelFellow at ELLIS Institut Tübingen, Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de

Dmitry Kobak

University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Machine Learning Unsupervised Learning Manifold learning Transcriptomics Computational Neuroscience


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The art of using t-SNE for single-cell transcriptomics D Kobak, P Berens Nature communications 10 (1), 5416, 2019	737	2019
Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset A Karlinsky, D Kobak Elife 10, e69336, 2021	604*	2021
Demixed principal component analysis of neural population data D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ... elife 5, e10989, 2016	453	2016
Distributed and mixed information in monosynaptic inputs to dopamine neurons J Tian, R Huang, JY Cohen, F Osakada, D Kobak, CK Machens, ... Neuron 91 (6), 1374-1389, 2016	236	2016
A multimodal cell census and atlas of the mammalian primary motor cortex Principal manuscript editors, Analysis coordination, ... Nature 598 (7879), 86-102, 2021	226	2021
Phenotypic variation of transcriptomic cell types in mouse motor cortex F Scala, D Kobak, M Bernabucci, Y Bernaerts, CR Cadwell, JR Castro, ... Nature 598 (7879), 144-150, 2021	211*	2021
Initialization is critical for preserving global data structure in both t-SNE and UMAP D Kobak, GC Linderman Nature biotechnology 39 (2), 156-157, 2021	208*	2021
Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas F Scala, D Kobak, S Shan, Y Bernaerts, S Laturnus, CR Cadwell, ... Nature communications 10 (1), 1-12, 2019	134*	2019
The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization D Kobak, J Lomond, B Sanchez Journal of Machine Learning Research 21 (169), 1-16, 2020	98	2020
Excess mortality reveals Covid's true toll in Russia D Kobak Significance (Oxford, England) 18 (1), 16, 2021	77	2021
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data J Lause, P Berens, D Kobak Genome biology 22 (1), 1-20, 2021	70	2021
Attraction-repulsion spectrum in neighbor embeddings JN Böhm, P Berens, D Kobak The Journal of Machine Learning Research 23 (1), 4118-4149, 2022	67*	2022
Integer percentages as electoral falsification fingerprints D Kobak, S Shpilkin, MS Pshenichnikov	67	2016
Statistical fingerprints of electoral fraud? D Kobak, S Shpilkin, MS Pshenichnikov Significance 13 (4), 20-23, 2016	60*	2016
Patch-seq: Past, present, and future M Lipovsek, C Bardy, CR Cadwell, K Hadley, D Kobak, SJ Tripathy Journal of Neuroscience 41 (5), 937-946, 2021	57	2021
Statistical anomalies in 2011-2012 Russian elections revealed by 2D correlation analysis D Kobak, S Shpilkin, MS Pshenichnikov arXiv preprint arXiv:1205.0741, 2012	45	2012
Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex CR Cadwell, F Scala, PG Fahey, D Kobak, S Mulherkar, FH Sinz, ... Elife 9, e52951, 2020	39*	2020
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations D Kobak, G Linderman, S Steinerberger, Y Kluger, P Berens Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019	37	2019
A systematic evaluation of interneuron morphology representations for cell type discrimination S Laturnus, D Kobak, P Berens Neuroinformatics 18, 591-609, 2020	28*	2020
Sparse reduced-rank regression for exploratory visualisation of paired multivariate data D Kobak, Y Bernaerts, MA Weis, F Scala, AS Tolias, P Berens Journal of the Royal Statistical Society Series C: Applied Statistics 70 (4 …, 2021	24*	2021

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