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Mateusz Garbulowski

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

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Plos One|February 8, 2017
Coalescence computations for large samples drawn from populations of time-varying sizesAndrzej Polanski, Agnieszka Szczesna, Mateusz Garbulowski, et al.
Bioinformatics (Oxford, England)|June 9, 2025
BiGSM: Bayesian inference of gene regulatory network via sparse modellingHang Qin, Mateusz Garbulowski, Erik L L Sonnhammer, 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|March 15, 2021
Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum DisorderMateusz Garbulowski, Karolina Smolinska, Klev Diamanti, et al.
Scientific Reports|June 26, 2026
Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancerMateusz Garbulowski, Riccardo Mosca, Carlos J Gallardo-Dodd, 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.
BMC Bioinformatics|March 7, 2021
R.ROSETTA: an interpretable machine learning frameworkMateusz Garbulowski, Klev Diamanti, Karolina Smolińska, et al.
NAR Genomics and Bioinformatics|September 19, 2024
GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell dataMateusz Garbulowski, Thomas Hillerton, Daniel Morgan, et al.
Cancers|February 25, 2022
Machine Learning-Based Analysis of Glioma Grades Reveals Co-EnrichmentMateusz Garbulowski, Karolina Smolinska, Uğur Çabuk, et al.
Bioinformatics (Oxford, England)|June 12, 2016
RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing dataTomasz Stokowy, Mateusz Garbulowski, Torunn Fiskerstrand, et al.
Pageof 2

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

Sort By:
Pageof 2
Plos One|February 8, 2017
Coalescence computations for large samples drawn from populations of time-varying sizesAndrzej Polanski, Agnieszka Szczesna, Mateusz Garbulowski, et al.
Bioinformatics (Oxford, England)|June 9, 2025
BiGSM: Bayesian inference of gene regulatory network via sparse modellingHang Qin, Mateusz Garbulowski, Erik L L Sonnhammer, 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|March 15, 2021
Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum DisorderMateusz Garbulowski, Karolina Smolinska, Klev Diamanti, et al.
Scientific Reports|June 26, 2026
Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancerMateusz Garbulowski, Riccardo Mosca, Carlos J Gallardo-Dodd, 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.
BMC Bioinformatics|March 7, 2021
R.ROSETTA: an interpretable machine learning frameworkMateusz Garbulowski, Klev Diamanti, Karolina Smolińska, et al.
NAR Genomics and Bioinformatics|September 19, 2024
GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell dataMateusz Garbulowski, Thomas Hillerton, Daniel Morgan, et al.
Cancers|February 25, 2022
Machine Learning-Based Analysis of Glioma Grades Reveals Co-EnrichmentMateusz Garbulowski, Karolina Smolinska, Uğur Çabuk, et al.
Bioinformatics (Oxford, England)|June 12, 2016
RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing dataTomasz Stokowy, Mateusz Garbulowski, Torunn Fiskerstrand, et al.
Pageof 2