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Andreas Tjärnberg

Showing results (1-10 of 26) with videos related to

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Nature Communications|February 14, 2020
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoderSanjiv K Dwivedi, Andreas Tjärnberg, Jesper Tegnér, et al.
Bioinformatics (Oxford, England)|September 1, 2018
A generalized framework for controlling FDR in gene regulatory network inferenceDaniel Morgan, Andreas Tjärnberg, Torbjörn E M Nordling, et al.
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|May 7, 2013
Optimal sparsity criteria for network inferenceAndreas Tjärnberg, Torbjörn E M Nordling, Matthew Studham, et al.
Nucleic Acids Research|November 24, 2011
Comparative interactomics with Funcoup 2.0Andrey Alexeyenko, Thomas Schmitt, Andreas Tjärnberg, et al.
Molecular Biosystems|November 8, 2014
Avoiding pitfalls in L1-regularised inference of gene networksAndreas Tjärnberg, Torbjörn E M Nordling, Matthew Studham, et al.
Bioinformatics (Oxford, England)|June 17, 2014
Functional association networks as priors for gene regulatory network inferenceMatthew E Studham, Andreas Tjärnberg, Torbjörn E M Nordling, et al.
Molecular Biosystems|May 10, 2017
GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data propertiesAndreas Tjärnberg, Daniel C Morgan, Matthew Studham, et al.
Genome Biology|January 18, 2024
Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceAndreas Tjärnberg, Maggie Beheler-Amass, Christopher A Jackson, et al.
Biorxiv : the Preprint Server for Biology|February 13, 2023
Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceAndreas Tjärnberg, Maggie Beheler-Amass, Christopher A Jackson, et al.
Scientific Reports|October 3, 2022
Knowledge of the perturbation design is essential for accurate gene regulatory network inferenceDeniz Seçilmiş, Thomas Hillerton, Andreas Tjärnberg, et al.
Pageof 3

Showing results (1-10 of 26) with videos related to

Sort By:
Pageof 3
Nature Communications|February 14, 2020
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoderSanjiv K Dwivedi, Andreas Tjärnberg, Jesper Tegnér, et al.
Bioinformatics (Oxford, England)|September 1, 2018
A generalized framework for controlling FDR in gene regulatory network inferenceDaniel Morgan, Andreas Tjärnberg, Torbjörn E M Nordling, et al.
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|May 7, 2013
Optimal sparsity criteria for network inferenceAndreas Tjärnberg, Torbjörn E M Nordling, Matthew Studham, et al.
Nucleic Acids Research|November 24, 2011
Comparative interactomics with Funcoup 2.0Andrey Alexeyenko, Thomas Schmitt, Andreas Tjärnberg, et al.
Molecular Biosystems|November 8, 2014
Avoiding pitfalls in L1-regularised inference of gene networksAndreas Tjärnberg, Torbjörn E M Nordling, Matthew Studham, et al.
Bioinformatics (Oxford, England)|June 17, 2014
Functional association networks as priors for gene regulatory network inferenceMatthew E Studham, Andreas Tjärnberg, Torbjörn E M Nordling, et al.
Molecular Biosystems|May 10, 2017
GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data propertiesAndreas Tjärnberg, Daniel C Morgan, Matthew Studham, et al.
Genome Biology|January 18, 2024
Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceAndreas Tjärnberg, Maggie Beheler-Amass, Christopher A Jackson, et al.
Biorxiv : the Preprint Server for Biology|February 13, 2023
Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inferenceAndreas Tjärnberg, Maggie Beheler-Amass, Christopher A Jackson, et al.
Scientific Reports|October 3, 2022
Knowledge of the perturbation design is essential for accurate gene regulatory network inferenceDeniz Seçilmiş, Thomas Hillerton, Andreas Tjärnberg, et al.
Pageof 3