R.ROSETTA: an interpretable machine learning framework

Garbulowski, Mateusz; Diamanti, Klev; Smolinska, Karolina; Baltzer, Nicholas; Stoll, Patricia; Bornelöv, Susanne; Øhrn, Aleksander; Feuk, Lars; Komorowski, Jan

doi:10.1101/625905

Cited by 4 publications

(4 citation statements)

References 31 publications

(11 reference statements)

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“…After selecting the most important features from high-dimensional data, we performed interpretable machine learning using the R.ROSETTA framework 26 . Subsequently, rule-based models were constructed and co-predictive features for each dataset were estimated.…”

Section: Interpretable Machine Learning For Identification Of Co-predictive Biomarkers In Amlmentioning

confidence: 99%

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

et al. 2022

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During the last decade, numerous studies have been carried out to exploit the complexity of genomic and transcriptomic lesions driving acute myeloid leukemia (AML) initiation. These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples are, however, sparse, meanwhile relapse and therapy resistance represent the main challenge in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1-downregulation and DPEP1-upregulation were associated with AML relapse both in adults and children. Finally, machine learning and network-based analysis identified overexpressed CD6 and downregulated INSR as highly co-predictive genes depicting important relapse-associated characteristics among adult AML patients. Our findings point towards the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments, to improve cure rates in AML.

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Section: Interpretable Machine Learning For Identification Of Co-predictive Biomarkers In Amlmentioning

confidence: 99%

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

et al. 2022

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“…Based on these basic measurements, other statistical values can be estimated, such as rule value of p ( Garbulowski et al, 2020 ). Additionally, the length of a rule is also an important factor.…”

Section: Methodsmentioning

confidence: 99%

“…To construct legible classifiers, we used a rule-based framework that receives a decision table as input and generates IML model as output. The modeling was performed with R package R.ROSETTA , which is a wrapper of the ROSETTA system ( Øhrn and Komorowski, 1997 ; Garbulowski et al, 2020 ). The IML modeling was performed using 10-fold CV and the standard voting method.…”

Section: Methodsmentioning

confidence: 99%

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Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

et al. 2021

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Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

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R.ROSETTA: an interpretable machine learning framework

Cited by 4 publications

References 31 publications

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

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