oposSOM-Browser: an interactive tool to explore omics data landscapes in health science.
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BiBTeXBackground: oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization.
Results: These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular 'portrait landscapes', of associated phenotype diversity, and signalling pathway activation patterns.
Conclusion: The oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at www.izbi.uni-leipzig.de/opossom-browser .
Keywords: Interactive data analysis; Results browser; Transcriptomics.
SEEK ID: https://armlifebank.am/publications/120
PubMed ID: 33076824
DOI: 10.1186/s12859-020-03806-w
Projects: ML approaches for omic data analysis
Publication type: Journal
Journal: BMC bioinformatics
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Created: 10th Sep 2025 at 10:51
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Projects: Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Biological pathway activity analysis, Mental disorders and aging brain, ML approaches for omic data analysis, Armenian Wine Genome Program
Institutions: Armenian Bioinformatics Institute, Interdisciplinary Center for Bioinformatics (IZBI)
https://orcid.org/0000-0002-2242-4678
Projects: Biological pathway activity analysis, Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue, ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing
Institutions: Institute of Molecular Biology NAS RA
https://orcid.org/0000-0002-6875-2482
Siras Hakobyan is currently a Junior Researcher and PhD student in the Research Group of Bioinformatics at the Institute of Molecular Biology NAS RA in Yerevan, Armenia. He received his Master's and Bachelor's degrees in Bioinformatics from Yerevan State University, completing his studies in 2018 and 2016. His PhD project focuses on pathway-centered analysis of high-throughput omics data, particularly examining pathway activity states in cancers based on pathway topology, gene expression, and ...
Projects: Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics, Development of induced pluripotent stem cell bank of patients with Familial Mediterranean Fever, Molecular Profiling of Cancer Metastases, Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue, Molecular characterization of cancers with long-read RNA sequencing, ML approaches for omic data analysis, Mental disorders and aging brain, Armenian Wine Genome Program, Functional Genomics of Vine, COVID-19, Genetic History of Armenians, Molecular epidemiology of multi-drug resistance, Multi-Omics Molecular Diversity of Cancers
Institutions: Russian-Armenian University, Institute of Molecular Biology NAS RA
https://orcid.org/0009-0009-7992-1366
This Programme focuses on the development, implementation, and application of bioinformatics tools for the analysis of large-scale genomic datasets. It aims to facilitate the processing, integration, and interpretation of high-throughput sequencing data, including whole-genome, transcriptome, and epigenome analyses. The Programme encompasses computational approaches for variant discovery, gene expression profiling, network biology, and multi-omics data integration.
Projects: Biological pathway activity analysis, ML approaches for omic data analysis, Tools for Telomere Biology
Web page: Not specified
This project focuses on developing and applying machine learning (ML) methodologies to analyze complex omic datasets, including genomics, transcriptomics, proteomics, and metabolomics. By leveraging advanced ML techniques, the project aims to uncover novel biological insights, identify biomarkers, and enhance our understanding of molecular mechanisms underlying various diseases. The initiative is a collaborative effort maintained by the Institute of Molecular Biology NAS RA (IMB), ...
Programme: Bioinformatics Tools for Analysis of Big Genomic Data
Public web page: Not specified
