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  3. Magmaflow: A Desktop Platform For Artificial Intelligence-driven Expression Analysis.
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  3. Magmaflow: A Desktop Platform For Artificial Intelligence-driven Expression Analysis.

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Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics
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MagmaFlow: A desktop platform for artificial intelligence-driven expression analysis.

Carlos E Buss1, Ao Li1, Eduardo H Gilglioni1

  • 1Signal Transduction and Metabolism Laboratory, Université libre de Bruxelles, Belgium.

FEBS Open Bio
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

MagmaFlow integrates literature evidence and pathway analysis for multi-omics data interpretation. This tool enhances volcano plots, transforming static data into dynamic biological insights with AI-powered features.

Keywords:
Alcohol‐associated liver diseaseArtificial intelligenceComputational biologyData visualizationVolcano plots

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics
  • Transcriptomics
  • Proteomics

Background:

  • Volcano plots are common for visualizing multi-omics data but often lack integration with other biological evidence.
  • Existing frameworks for combining literature, pathway associations, and functional annotation with differential expression data are limited.
  • There is a need for tools that bridge the gap between raw multi-omics data and actionable biological interpretation.

Purpose of the Study:

  • To present MagmaFlow, a novel cross-platform application for enhanced multi-omics data analysis.
  • To integrate literature evidence, pathway associations, and functional annotation into volcano plot interpretation.
  • To transform static volcano plot visualization into a dynamic tool for biological discovery.

Main Methods:

  • Developed MagmaFlow, featuring literature-based gene scoring using PubMed via PubTator3.
  • Implemented interactive pathway-to-volcano mapping with multi-layer circle plots for visualizing pathway enrichment.
  • Integrated synchronized cross-view updates, smart label positioning, drag-and-drop annotation, and customizable styles for publication-quality figures.

Main Results:

  • MagmaFlow provides context-specific gene relevance ranking by retrieving associations from scientific literature.
  • The application visualizes pathway enrichment and cross-pathway membership interactively, synchronized with user selections.
  • Demonstrated the ability to convert differential expression data into actionable biological insights using AI-powered literature contextualization.

Conclusions:

  • MagmaFlow offers a unique solution for integrating diverse biological data types for enhanced interpretation.
  • The tool facilitates dynamic biological interpretation by enabling interactive exploration of multi-omics data.
  • MagmaFlow represents a significant advancement in converting complex datasets into biologically meaningful and actionable findings.