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Keywords = supervised SOM portraying

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Article
Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes
by Maria Nikoghosyan, Henry Loeffler-Wirth, Suren Davidavyan, Hans Binder and Arsen Arakelyan
BioMedInformatics 2022, 2(1), 62-76; https://doi.org/10.3390/biomedinformatics2010004 - 27 Dec 2021
Cited by 4 | Viewed by 5234
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Current Trends and Developments in Bioinformatics and Statistical Research from a Biomedical Aspect)
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