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Thomas Hillerton

Showing results (1-10 of 11) with videos related to

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Nucleic Acids Research|May 24, 2022
GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methodsDeniz Seçilmiş, Thomas Hillerton, Erik L L Sonnhammer
Bioinformatics (Oxford, England)|May 12, 2021
Inferring the experimental design for accurate gene regulatory network inferenceDeniz Seçilmiş, Thomas Hillerton, Sven Nelander, et al.
Bioinformatics (Oxford, England)|February 17, 2022
Fast and accurate gene regulatory network inference by normalized least squares regressionThomas Hillerton, Deniz Seçilmiş, Sven Nelander, et al.
Bioinformatics (Oxford, England)|March 24, 2025
Topology-based metrics for finding the optimal sparsity in gene regulatory network inferenceNils Lundqvist, Mateusz Garbulowski, Thomas Hillerton, et al.
Frontiers in Genetics|February 28, 2022
Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward LoopsErik K Zhivkoplias, Oleg Vavulov, Thomas Hillerton, et al.
Frontiers in Aging|February 7, 2025
Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and diseaseDavid Martínez-Enguita, Thomas Hillerton, Julia Åkesson, et al.
Journal of Internal Medicine|February 25, 2026
Epigenetic analyses suggest different pathways during pregnancy for development of Type 1 diabetes in children with high versus low-neutral human leukocyte antigen-riskShamila D Alipoor, Angelica Ahrens, Julia Åkesson, et al.
Bioinformatics Advances|May 5, 2026
GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression dataThomas Hillerton, Anton Björk, Nils Lundqvist, 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.
NPJ Systems Biology and Applications|November 10, 2020
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative dataDeniz Seçilmiş, Thomas Hillerton, Daniel Morgan, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Nucleic Acids Research|May 24, 2022
GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methodsDeniz Seçilmiş, Thomas Hillerton, Erik L L Sonnhammer
Bioinformatics (Oxford, England)|May 12, 2021
Inferring the experimental design for accurate gene regulatory network inferenceDeniz Seçilmiş, Thomas Hillerton, Sven Nelander, et al.
Bioinformatics (Oxford, England)|February 17, 2022
Fast and accurate gene regulatory network inference by normalized least squares regressionThomas Hillerton, Deniz Seçilmiş, Sven Nelander, et al.
Bioinformatics (Oxford, England)|March 24, 2025
Topology-based metrics for finding the optimal sparsity in gene regulatory network inferenceNils Lundqvist, Mateusz Garbulowski, Thomas Hillerton, et al.
Frontiers in Genetics|February 28, 2022
Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward LoopsErik K Zhivkoplias, Oleg Vavulov, Thomas Hillerton, et al.
Frontiers in Aging|February 7, 2025
Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and diseaseDavid Martínez-Enguita, Thomas Hillerton, Julia Åkesson, et al.
Journal of Internal Medicine|February 25, 2026
Epigenetic analyses suggest different pathways during pregnancy for development of Type 1 diabetes in children with high versus low-neutral human leukocyte antigen-riskShamila D Alipoor, Angelica Ahrens, Julia Åkesson, et al.
Bioinformatics Advances|May 5, 2026
GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression dataThomas Hillerton, Anton Björk, Nils Lundqvist, 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.
NPJ Systems Biology and Applications|November 10, 2020
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative dataDeniz Seçilmiş, Thomas Hillerton, Daniel Morgan, et al.
Pageof 2