Author

John Wiedenhoeft

University Hospital LeipzigORCID 0000-0002-6935-1517Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Molecular Biology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Alexander SchliepiD· 2 shared Andreas BeckeriD· 1 shared Andreas HeinziD· 1 shared Antoine GrigisiD· 1 shared Arun L.W. BokdeiD· 1 shared Bader ChaaraniiD· 1 shared Bernd IttermanniD· 1 shared Betteke Maria van NoortiD· 1 shared

Activity by Year

2016-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersNeuroscience

Alexander SchliepORCID

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Author profile →

3 papers

Recent·Most cited

Reannotation of the zebra finch genome reveals undiscovered transcriptional complexity

Hugues Richard, John Wiedenhoeft, Constance Scharff, Iris Adam

The paper reannotates the zebra finch genome using transcriptome data, revealing new transcriptional units and refining gene models.

bioRxiv · Jan 9, 2024 · PreprintMolecular Biology

Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment.

Nicholas Judd, Bruno Sauce, John Wiedenhoeft, Jeshua Tromp, Bader Chaarani, Alexander Schliep, Betteke Maria van Noort, Jani Penttilä, Yvonne Grimmer, Corinna Insensee, Andreas Becker, Tobias Banaschewski, Arun L.W. Bokde, Erin Burke Quinlan, Sylvane Desrivières, , Torkel Klingberg

This study examines the independent effects of socioeconomic status and genetic factors on brain development and cognition in adolescents, finding distinct associations with cortical surface area and working memory.

Proceedings of the National Academy of Sciences · May 19, 2020Peer-reviewedNeuroscience

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

John Wiedenhoeft, Eric Brugel, Alexander Schliep

This paper introduces a Bayesian Hidden Markov Model with wavelet compression for faster and more accurate copy number variant detection in array CGH data.

PLOS Computational Biology · May 13, 2016Peer-reviewedBioinformatics