Author

Sanne Schoenmakers

Maastricht UniversityORCID 0000-0001-7429-0694Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Alard F. RoebroeckiD· 1 shared Francisco J. FritziD· 1 shared Jan‐Mathijs SchoffeleniD· 1 shared Katja SeeligeriD· 1 shared Marcel A. J. van GerveniD· 1 shared Matthias FritscheiD· 1 shared Robbert HarmsiD· 1 shared Sander BoschiD· 1 shared

Activity by Year

2017-2019 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 18 mutual co-author links

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1 shared paperNeuroscience

Alard F. RoebroeckORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintMay 27, 2019

Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies

Robbert Harms, Francisco J. Fritz, Sanne Schoenmakers, Alard F. Roebroeck

This paper introduces a method to quantify uncertainty in diffusion MRI models, showing it improves artifact detection and statistical power in group studies.

5 citations

statistical model

𝕏 PostBluesky

NeuroscienceNeuroImagePeer-reviewedJul 16, 2017

CNN-based Encoding and Decoding of Visual Object Recognition in Space and Time

Katja Seeliger, Matthias Fritsche, Umut Güçlü…·Marcel A. J. van Gerven

The paper uses CNNs to model visual object recognition in MEG data, showing a feedforward sweep across the visual hierarchy and achieving high decoding accuracy.

121 citationstop 2%

humanelectronic health records

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