Mateusz Garbulowski

Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer

Garbulowski, Mateusz; Mosca, Riccardo; Gallardo-Dodd, Carlos J.; Kutter, Claudia et al.

Part of Scientific Reports, 2026

• DOI for Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer
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BiGSM: Bayesian inference of gene regulatory network via sparse modelling

Qin, Hang; Garbulowski, Mateusz; Sonnhammer, Erik L. L.; Chatterjee, Saikat

Part of Bioinformatics, 2025

• DOI for BiGSM: Bayesian inference of gene regulatory network via sparse modelling
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GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Garbulowski, Mateusz; Hillerton, Thomas; Morgan, Daniel; Secilmis, Deniz et al.

Part of NAR Genomics and Bioinformatics, 2024

• DOI for GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data
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Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Garbulowski, Mateusz; Smolinska Garbulowska, Karolina; Cabuk, Ugur; Younes, Sara et al.

Part of Cancers, 2022

• DOI for Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
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Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Stratmann, Svea; Yones, Sara A.; Garbulowski, Mateusz; Sun, Jitong et al.

Part of Blood Advances, p. 152-164, 2022

• DOI for Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression
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Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer

Garbulowski, Mateusz; Mosca, Riccardo; Gallardo-Dodd, Carlos J.; Kutter, Claudia et al.

Part of Scientific Reports, 2026

• DOI for Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer
• Download full text (pdf) of Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer

BiGSM: Bayesian inference of gene regulatory network via sparse modelling

Qin, Hang; Garbulowski, Mateusz; Sonnhammer, Erik L. L.; Chatterjee, Saikat

Part of Bioinformatics, 2025

• DOI for BiGSM: Bayesian inference of gene regulatory network via sparse modelling
• Download full text (pdf) of BiGSM: Bayesian inference of gene regulatory network via sparse modelling

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Garbulowski, Mateusz; Hillerton, Thomas; Morgan, Daniel; Secilmis, Deniz et al.

Part of NAR Genomics and Bioinformatics, 2024

• DOI for GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data
• Download full text (pdf) of GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Garbulowski, Mateusz; Smolinska Garbulowska, Karolina; Cabuk, Ugur; Younes, Sara et al.

Part of Cancers, 2022

• DOI for Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
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Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Stratmann, Svea; Yones, Sara A.; Garbulowski, Mateusz; Sun, Jitong et al.

Part of Blood Advances, p. 152-164, 2022

• DOI for Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression
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R.ROSETTA: an interpretable machine learning framework

Garbulowski, Mateusz; Diamanti, Klev; Smolinska, Karolina; Baltzer, Nicholas et al.

Part of BMC Bioinformatics, 2021

• DOI for R.ROSETTA: an interpretable machine learning framework
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Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

Garbulowski, Mateusz; Smolinska Garbulowska, Karolina; Diamanti, Klev; Pan, Gang et al.

Part of Frontiers in Genetics, 2021

• DOI for Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder
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Coalescence computations for large samples drawn from populations of time-varying sizes

Polanski, Andrzej; Szczesna, Agnieszka; Garbulowski, Mateusz; Kimmel, Marek

Part of PLOS ONE, 2017

• DOI for Coalescence computations for large samples drawn from populations of time-varying sizes
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Patterns in big data bioinformatics: Understanding complex diseases with interpretable machine learning

Garbulowski, Mateusz

2021

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Consensus Approach for Detection of Cancer Somatic Mutations

Sieradzka, Katarzyna; Leszczorz, Kinga; Garbulowski, Mateusz; Polanski, Andrzej

Part of Man-Machine Interactions 5, ICMM 2017, p. 163-171, 2018

• DOI for Consensus Approach for Detection of Cancer Somatic Mutations

SUPPLEMENTARY MATERIAL: VisuNet: an interactive tool for rule network visualization of rule-based learning models

Smolinska Garbulowska, Karolina; Garbulowski, Mateusz; Diamanti, Klev; Davoy, Xavier et al.

2021

• Download full text (pdf) of SUPPLEMENTARY MATERIAL: VisuNet: an interactive tool for rule network visualization of rule-based learning models

SUPPLEMENTARY MATERIAL: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression

Stratmann, Svea; Yones, Sara A.; Garbulowski, Mateusz; Sun, Jitong et al.

2021

SUPPLEMENTARY MATERIAL: Machine learning-based analysis of glioma grades reveals co-enrichment

Garbulowski, Mateusz; Smolinska Garbulowska, Karolina; Çabuk, Uğur; Yones, Sara A. et al.

2021

SUPPLEMENTAL INFORMATION FOR: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression

Stratmann, Svea; Yones, Sara A.; Garbulowski, Mateusz; Sun, Jitong et al.

2020

• Download full text (pdf) of SUPPLEMENTAL INFORMATION FOR: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression

Machine learning-based analysis of glioma grades reveals co-enrichment

Garbulowski, Mateusz; Smolinska, Karolina; Çabuk, Uğur; Yones, Sara A. et al.

Part of SUPPLEMENTARY MATERIAL: Machine learning-based analysis of glioma grades reveals co-enrichment

VisuNet: an interactive tool for rule network visualization of rule-based learning models

Smolinska, Karolina; Garbulowski, Mateusz; Diamanti, Klev; Davoy, Xavier et al.

Part of SUPPLEMENTARY MATERIAL: VisuNet: an interactive tool for rule network visualization of rule-based learning models