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Updated: Jul 8, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
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List2Net: Linking multiple lists of biological data in a network context.

Sotiroula Afxenti1, Marios Tomazou1, George Tsouloupas2

  • 1Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus.

Computational and Structural Biotechnology Journal
|December 11, 2023
PubMed
Summary

List2Net is a new web tool that transforms biological data lists into interactive networks, enabling comprehensive analysis of complex biological relationships and insights. This approach offers a holistic view for diverse datasets, unlike traditional methods limited by data type and number of lists.

Keywords:
Biological network constructionMulti-layer data representationNetwork analysisNetwork representation of lists commonalities

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Network Science

Background:

  • Comparing biological data lists (e.g., gene symbols, sequences, omics profiles) is crucial in research.
  • Existing visualization tools like Venn diagrams are limited by data type and the number of lists.
  • Network representations offer a versatile approach for analyzing complex relationships across multiple lists.

Purpose of the Study:

  • To introduce List2Net, a web-based tool for generating and analyzing biological data networks.
  • To address the gap in tools capable of handling arbitrary numbers of diverse biological data lists.
  • To facilitate holistic insights into biological conditions through network analysis.

Main Methods:

  • List2Net processes diverse biological data types, including named entities, sequences, and omics profiles.
  • The tool calculates similarities (edges) between lists (nodes) and generates network visualizations.
  • It supports single-layer and multi-layer network modes for comprehensive analysis.

Main Results:

  • List2Net successfully translates various biological data lists into network structures.
  • The tool provides options for exporting network visualizations and calculated metrics.
  • A case study on Multiple Sclerosis datasets demonstrates its utility in generating disease-to-disease subnetworks.

Conclusions:

  • List2Net provides a fast, lightweight, and informative solution for biological data network analysis.
  • It enables the discovery of novel insights through network centralities, clusters, and motifs.
  • The tool enhances the understanding of complex biological conditions by integrating multi-source data.