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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Omics BioAnalytics: an RShiny application for multimodal biomarker panel discovery and assessment.

Josh Dyce^1,2,3, Lea Rieskamp⁴, Scott J Tebbutt^2,3,5

¹Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada.

Bioinformatics Advances

|January 12, 2026

Summary

This summary is machine-generated.

Omics BioAnalytics integrates multiple omics data types for enhanced disease insights. This R Shiny platform offers reproducible multimodal analysis and biomarker discovery through web and voice interfaces.

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Area of Science:

Biomedical informatics
Computational biology
Data science in healthcare

Background:

High-dimensional molecular data from omics technologies (transcriptomics, proteomics, metabolomics) offer deep biological insights.
Integrating multiple data modalities, such as histology-omics and holter-omics, can significantly improve predictive model performance.
There is a need for user-friendly, reproducible platforms for multimodal omics data analysis.

Purpose of the Study:

To develop Omics BioAnalytics, an R Shiny platform for unified analysis, integration, and interpretation of diverse omics datasets.
To provide streamlined, reproducible, and user-friendly multimodal analytics for biomedical researchers.
To support the discovery of molecular signatures, predictive biomarkers, and therapeutic targets.

Main Methods:

Late integration using ensembles of elastic net models trained independently on each modality.
Predictions are averaged across datasets for robust analysis.
The platform utilizes interactive dashboards for exploration, differential expression, gene set analysis, and biomarker discovery.

Main Results:

Omics BioAnalytics enables unified analysis and interpretation of multimodal omics data.
Interactive dashboards facilitate exploration, differential expression, gene set analysis, and biomarker discovery.
Integrated voice-based querying via an Alexa Skill allows for rapid visualization and accessibility.

Conclusions:

Omics BioAnalytics provides accessible and reproducible multimodal analytics for biomedical researchers.
The platform supports the discovery of molecular signatures and predictive biomarkers.
Web and voice-enabled tools enhance the utility of omics data integration for biological discovery.