| 1. |
- Garbulowski, Mateusz, et al.
(author)
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Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder
- 2021
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In: Frontiers in Genetics. - : Frontiers Media S.A.. - 1664-8021. ; 12
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Journal article (peer-reviewed)abstract
- 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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| 3. |
- Garbulowski, Mateusz, et al.
(author)
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Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
- 2022
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In: Cancers. - : MDPI AG. - 2072-6694. ; 14:4
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Journal article (peer-reviewed)abstract
- Simple Summary Gliomas are heterogenous types of cancer, therefore the therapy should be personalized and targeted toward specific pathways. We developed a methodology that corrected strong batch effects from The Cancer Genome Atlas datasets and estimated glioma grade-specific co-enrichment mechanisms using machine learning. Our findings created hypotheses for annotations, e.g., pathways, that should be considered as therapeutic targets. Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.
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| 4. |
- Garbulowski, Mateusz
(author)
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Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. Specifically, we employed a rule-based ML to identify associations among features and a decision in a combinatorial manner, i.e. a co-prediction. We developed tools and methods that can be applied by a large community of bioinformaticians and proved their usability through a variety of studies.In paper I, we developed an R.ROSETTA package that provides an environment for rule-based ML relying on the rough sets. Basically, R.ROSETTA is an R wrapper of the ROSETTA toolkit; however, it extends its functions with various analytical solutions. The package was tested on a microarray gene expression case-control study of autism. Estimated models were highly accurate and provided lists of possible interactions among genes. Moreover, benchmarking revealed that R.ROSETTA was among the best performing rule- and decision tree-based methods.In paper II, we applied the R.ROSETTA together with a VisuNet package. We used both tools to perform a rule-based network analysis of autism spectrum disorder (ASD) subtypes. Here, we used microarray-based gene expression measures of ASD patients and controls from three data sets. We demonstrated that rule-based modelling is an efficient approach to merge multiple cohorts. Furthermore, we estimated centrality distances among produced subnetworks that revealed dissimilarities of ASD subtypes and controls. Finally, we discovered a highly probable interaction between EMC4 and TMEM30A genes.In paper III, we investigated our tools to perform an RNA-seq-based gene expression analysis of Acute Myeloid Leukemia (AML). We aimed at discovering gene expression patterns between the AML diagnosis and relapse. Specifically, we applied a rule-based network analysis to validate independent cohorts. Our study revealed that overexpressed CD6 and underexpressed INSR are highly co-predictive genes associated to the AML relapse. Finally, we demonstrated arc diagrams as a novel way of visualizing co-predictors.In paper IV, we analyzed glioma grading by performing a comprehensive ML analysis for RNA-seq data sets. We broadly preprocessed data sets and removed a strong batch effect that occurred between glioma grades. Afterwards, we performed ML evaluation on single-sample gene set enrichment scores that revealed topmost accurate collections and annotations that distinguish glioma grades. Among others, we found cell cycle, Fanconi anemia and cholesterol-related pathways associated to glioma progression. Finally, we discovered several co-enrichment mechanisms among annotations.
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| 5. |
- Garbulowski, Mateusz, et al.
(author)
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R.ROSETTA : an interpretable machine learning framework
- 2021
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In: BMC Bioinformatics. - : BioMed Central (BMC). - 1471-2105. ; 22:1
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Journal article (peer-reviewed)abstract
- BackgroundMachine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components.ResultsWe present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA. To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case–control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes.ConclusionsR.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.
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| 7. |
- Hillerton, Thomas, et al.
(author)
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GeneSNAKE: a Python package for benchmarking and simulation of gene regulatory networks and expression data.
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Other publication (other academic/artistic)abstract
- Understanding how genes interact with and regulate each other is a key challenge in systems biology. One of the primary methods to study this is through gene regulatory networks (GRNs). The field of GRN inference however faces many challenges, such as the complexity of gene regulation and high noise levels, which necessitates effective tools for evaluating inference methods. For this purpose, data that corresponds to a known GRN, from various conditions and experimental setups is necessary, which is only possible to attain via simulation. Existing tools for simulating data for GRN inference have limitations either in the way networks are constructed or data is produced, and are often not flexible for adjusting the algorithm or parameters. To overcome these issues we present GeneSNAKE, a Python package designed to allow users to generate biologically realistic GRNs, and from a GRN simulate expression data for benchmarking purposes. GeneSNAKE allows the user to control a wide range of network and data properties. GeneSNAKE improves on previous work in the field by adding a perturbation model that allows for a greater range of perturbation schemes along with the ability to control noise and modify the perturbation strength. For benchmarking, GeneSNAKE offers a number of functions both for comparing a true GRN to an inferred GRN, and to study properties in data and GRN models. These functions can in addition be used to study properties of biological data to produce simulated data with more realistic properties. GeneSNAKE is an open-source, comprehensive simulation and benchmarking package with powerful capabilities that are not combined in any other single package, and thanks to the Python implementation it is simple to extend and modify by a user.
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| 8. |
- Polanski, Andrzej, et al.
(author)
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Coalescence computations for large samples drawn from populations of time-varying sizes
- 2017
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:2
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Journal article (peer-reviewed)abstract
- We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.
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| 9. |
- Sieradzka, Katarzyna, et al.
(author)
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Consensus Approach for Detection of Cancer Somatic Mutations
- 2018
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In: Man-Machine Interactions 5, ICMM 2017. - Cham : Springer International Publishing. - 9783319677927 - 9783319677910 ; , s. 163-171
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Conference paper (peer-reviewed)abstract
- We present a consensus algorithm for detection of somatic mutations in cancer genomics data, based on integrating results of four published somatic mutation callers, MuTect2, MuSE, Varscan2 and Somatic Sniper. We generate consensus lists of cancer somatic mutations by using a simple voting mechanisms. Performances of cancer somatic mutations searching algorithms are verified by a quality index defined by the estimated proportion between driver and passenger mutations. We demonstrate, on the basis of three large NGS datasets from the TCGA database, that our consensus algorithm improves detection of cancer somatic mutations.
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