Author

Alexander Schliep

University of GothenburgORCID 0000-0002-3555-3188Unclaimed 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 Approach:Bayesian inference· 1 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1 Research Focus:software / tool· 1

Frequent Co-Authors

John WiedenhoeftiD· 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

2016-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

John WiedenhoeftORCID

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3 papers

Recent·Most cited

Empirical study on software and process quality in bioinformatics tools

Katalin Ferenc, Konrad Otto, Francisco Gomes de Oliveira Neto, Marcela Dávila López, Jennifer Horkoff, Alexander Schliep

This study evaluates software quality and process issues in HTS alignment tools, finding high maintainability but increasing technical debt and recommending improvements in tooling and documentation.

bioRxiv · Mar 10, 2022 · PreprintBioinformatics

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