E-LIB Suche - Ergebnisse für: Mateusz Garbulowski

1

Machine Learning-Based Analysis of Glioma Grades Reveals Co..:

Garbulowski, Mateusz ; Smolinska, Karolina ; Çabuk, Uğur...
Cancers. 14 (2022) 4 - p. 1014 , 2022

Link: https://doi.org/10.3390/..

2

Transcriptomic analysis reveals proinflammatory signatures ..:

Stratmann, Svea ; Yones, Sara A. ; Garbulowski, Mateusz...
Blood Advances. 6 (2022) 1 - p. 152-164 , 2022

Link: https://doi.org/10.1182/..

3

Interpretable Machine Learning Reveals Dissimilarities Betw..:

Garbulowski, Mateusz ; Smolinska, Karolina ; Diamanti, Klev...
Frontiers in Genetics. 12 (2021) - p. , 2021

Link: https://doi.org/10.3389/..

4

R.ROSETTA: an interpretable machine learning framework:

Garbulowski, Mateusz ; Diamanti, Klev ; Smolińska, Karolina...
BMC Bioinformatics. 22 (2021) 1 - p. , 2021

Link: https://doi.org/10.1186/..

5

Coalescence computations for large samples drawn from popul..:

Polanski, Andrzej ; Szczesna, Agnieszka ; Garbulowski, Mateusz..
PLOS ONE. 12 (2017) 2 - p. e0170701 , 2017

Link: https://doi.org/10.1371/..

6

RareVariantVis: new tool for visualization of causative var..:

Stokowy, Tomasz ; Garbulowski, Mateusz ; Fiskerstrand, Torunn...
Bioinformatics. 32 (2016) 19 - p. 3018-3020 , 2016

Link: https://doi.org/10.1093/..

7

Machine Learning-Based Analysis of Glioma Grades Reveals Co..:

Mateusz Garbulowski ; Karolina Smolinska ; Uğur Çabuk...
Cancer Pathophysiology. , 2022

Link: https://doi.org/10.3390/..

8

Machine Learning-Based Analysis of Glioma Grades Reveals Co..:

Mateusz Garbulowski ; Karolina Smolinska ; Uğur Çabuk...
https://www.mdpi.com/2072-6694/14/4/1014. , 2022

Link: https://doi.org/10.3390/..

9

Data_Sheet_1_Interpretable Machine Learning Reveals Dissimi..:

Mateusz Garbulowski ; Karolina Smolinska ; Klev Diamanti...
doi:10.3389/fgene.2021.618277.s001. , 2021

Link: https://doi.org/10.3389/..

10

R.ROSETTA: an interpretable machine learning framework:

Mateusz Garbulowski ; Klev Diamanti ; Karolina Smolińska...
https://doi.org/10.1186/s12859-021-04049-z. , 2021

Link: https://doi.org/10.1186/..

11

Interpretable Machine Learning Reveals Dissimilarities Betw..:

Mateusz Garbulowski ; Karolina Smolinska ; Klev Diamanti...
https://www.frontiersin.org/articles/10.3389/fgene.2021.618277/full. , 2021

Link: https://doi.org/10.3389/..

12

Table_1_Interpretable Machine Learning Reveals Dissimilarit..:

Mateusz Garbulowski ; Karolina Smolinska ; Klev Diamanti...
doi:10.3389/fgene.2021.618277.s002. , 2021

Link: https://doi.org/10.3389/..

13

Coalescence computations for large samples drawn from popul..:

Andrzej Polanski ; Agnieszka Szczesna ; Mateusz Garbulowski.
http://europepmc.org/articles/PMC5295683?pdf=render. , 2017

Link: https://doi.org/10.1371/..

14

Additional file 1 of R.ROSETTA: an interpretable machine le..:

Mateusz Garbulowski (3723319) ; Klev Diamanti (10191254) ; Karolina Smolińska (10267010)...
https://figshare.com/articles/journal_contribution/Additional_file_1_of_R_ROSETTA_an_interpretable_machine_learning_framework/14176253. , 2021

Link: https://doi.org/10.6084/..

15

Table_1_Interpretable Machine Learning Reveals Dissimilarit..:

Mateusz Garbulowski (3723319) ; Karolina Smolinska (4320805) ; Klev Diamanti (10191254)...
https://figshare.com/articles/dataset/Table_1_Interpretable_Machine_Learning_Reveals_Dissimilarities_Between_Subtypes_of_Autism_Spectrum_Disorder_xlsx/14110709. , 2021

Link: https://doi.org/10.3389/..