Molecular subtyping of bladder cancer using <scp>K</scp>ohonen self‐organizing maps

Borkowska, Edyta; Kruk, Andrzej; Jędrzejczyk, Adam; Rożniecki, Marek; Jabłonowski, Zbigniew; Traczyk, Magdalena; Constantinou, Maria; Banaszkiewicz, Monika; Pietrusiński, Michaƚ; Sosnowski, Marek; Hamdy, Freddie C.; Schmitz, Peter; Catto, James W.F.; Kałużewski, Bogdan

doi:10.1002/cam4.217

Cited by 19 publications

(4 citation statements)

References 37 publications

(38 reference statements)

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“…Self Organizing Maps (SOM) ( 14 ) are one of the most popular visualization tool nowadays. SOM is an Artificial Neural Network (ANN) proposed by Teuvo Kohonen ( 15 ) and, since then, it has been analyzed and employed extensively in a wide variety of domains, such medical ( 16 , 17 ), engineering applications ( 18 , 19 ) and even in the field of animal sciences ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

Gandia-Aguilo¹,

Cibrián²,

Soria³

et al. 2017

Med Oral

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BackgroundDisplaced maxillary permanent canine is one of the more frequent findings in canine eruption process and it’s easy to be outlined and early diagnosed by means of x-ray images. Late diagnosis frequently needs surgery to rescue the impacted permanent canine. In many cases, interceptive treatment to redirect canine eruption is needed. However, some patients treated by interceptive means end up requiring fenestration to orthodontically guide the canine to its normal occlusal position. It would be interesting, therefore, to discover the dental characteristics of patients who will need additional surgical treatment to interceptive treatment.Material and MethodsTo study the dental characteristics associated with canine impaction, conventional statistics have traditionally been used. This approach, although serving to illustrate many features of this problem, has not provided a satisfactory response or not provided an overall idea of the characteristics of these types of patients, each one of them with their own particular set of variables. Faced with this situation, and in order to analyze the problem of impaction despite interceptive treatment, we have used an alternative method for representing the variables that have an influence on this syndrome. This method is known as Self-Organizing Maps (SOM), a method used for analyzing problems with multiple variables.ResultsWe analyzed 78 patients with a PMC angulation higher than 100º. All of them were subject to interceptive treatment and in 21 cases it was necessary to undertake the above-mentioned fenestration to achieve the final eruption of the canine.ConclusionsIn this study, we describe the process of debugging variables and selecting the appropriate number of cells in SOM so as to adequately visualize the problem posed and the dental characteristics of patients with regard to a greater or lesser probability of the need for fenestration. Key words:Interceptive orthodontic treatment, altered eruption, impacted canines, neuronal networks, self-organizing maps.

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Section: Methodsmentioning

confidence: 99%

Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

Gandia-Aguilo¹,

Cibrián²,

Soria³

et al. 2017

Med Oral

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“…There are many other clustering and unsupervised learning approaches, two of the most frequently used for molecular subtyping are k-means and self-organizing maps (SOM) (Borkowska et al, 2014;Hartigan and Wong, 1979;Kohonen, 1989). These approaches can often create more definitive and interpretable clusters than hierarchical methods.…”

Section: Classifying Patients Into Subtypesmentioning

confidence: 99%

The Promise of Multi-Omics and Clinical Data Integration to Identify and Target Personalized Healthcare Approaches in Autism Spectrum Disorders

Higdon

Earl

Stanberry

et al. 2015

OMICS: A Journal of Integrative Biology

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Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

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“…The high-dimensional low sample size nature of modern -omics datasets necessitates application of dimensionality reduction and clustering approaches for their efficient analysis. The self-organizing maps (SOM) portrayal method implemented in the oposSOM package [1] has been proven to be a powerful approach for analysis of differential expression [2], molecular subtyping [3], and sample stratification [4]. SOM clusters gene expression profiles (vectors of gene expression values across samples) into miniclusters called meta-genes and projects high-dimensional data into two-dimensional maps.…”

Section: Introductionmentioning

confidence: 99%

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

et al. 2021

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The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

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Molecular subtyping of bladder cancer using Kohonen self‐organizing maps

Cited by 19 publications

References 37 publications

Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment

The Promise of Multi-Omics and Clinical Data Integration to Identify and Target Personalized Healthcare Approaches in Autism Spectrum Disorders

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

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