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.

Ingår i Scientific Reports, 2026

• DOI för Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer
• Ladda ner fulltext (pdf) av 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

Ingår i Bioinformatics, 2025

• DOI för BiGSM: Bayesian inference of gene regulatory network via sparse modelling
• Ladda ner fulltext (pdf) av 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.

Ingår i NAR Genomics and Bioinformatics, 2024

• DOI för GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data
• Ladda ner fulltext (pdf) av 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.

Ingår i Cancers, 2022

• DOI för Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
• Ladda ner fulltext (pdf) av Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

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

Ingår i Blood Advances, s. 152-164, 2022

• DOI för Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression
• Ladda ner fulltext (pdf) av Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

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.

Ingår i Scientific Reports, 2026

• DOI för Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer
• Ladda ner fulltext (pdf) av 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

Ingår i Bioinformatics, 2025

• DOI för BiGSM: Bayesian inference of gene regulatory network via sparse modelling
• Ladda ner fulltext (pdf) av 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.

Ingår i NAR Genomics and Bioinformatics, 2024

• DOI för GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data
• Ladda ner fulltext (pdf) av 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.

Ingår i Cancers, 2022

• DOI för Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
• Ladda ner fulltext (pdf) av Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

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

Ingår i Blood Advances, s. 152-164, 2022

• DOI för Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression
• Ladda ner fulltext (pdf) av Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

R.ROSETTA: an interpretable machine learning framework

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

Ingår i BMC Bioinformatics, 2021

• DOI för R.ROSETTA: an interpretable machine learning framework
• Ladda ner fulltext (pdf) av R.ROSETTA: an interpretable machine learning framework

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

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

Ingår i Frontiers in Genetics, 2021

• DOI för Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder
• Ladda ner fulltext (pdf) av Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

Coalescence computations for large samples drawn from populations of time-varying sizes

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

Ingår i PLOS ONE, 2017

• DOI för Coalescence computations for large samples drawn from populations of time-varying sizes
• Ladda ner fulltext (pdf) av Coalescence computations for large samples drawn from populations of time-varying sizes

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

• Ladda ner fulltext (pdf) av 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

• Ladda ner fulltext (pdf) av SUPPLEMENTAL INFORMATION FOR: Transcriptomic analysis reveals pro-inflammatory signatures associated with acute myeloid leukemia progression

Patterns in big data bioinformatics: Understanding complex diseases with interpretable machine learning

Garbulowski, Mateusz

2021

• Ladda ner fulltext (pdf) av Patterns in big data bioinformatics: Understanding complex diseases with interpretable machine learning

Consensus Approach for Detection of Cancer Somatic Mutations

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

Ingår i Man-Machine Interactions 5, ICMM 2017, s. 163-171, 2018

• DOI för Consensus Approach for Detection of Cancer Somatic Mutations

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

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

Ingår i 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.

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