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Related Experiment Video

Updated: Jul 12, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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SOMMD: an R package for the analysis of molecular dynamics simulations using self-organizing map.

Stefano Motta1, Lara Callea1, Shaziya Ismail Mulla2

  • 1Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, 20126, Italy.

Bioinformatics (Oxford, England)
|May 15, 2025
PubMed
Summary

We developed SOMMD, an R package for analyzing molecular dynamics (MD) simulations using Self-Organising Maps (SOMs). This tool simplifies complex trajectory data interpretation, aiding in understanding biomolecular processes.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Data Science

Background:

  • Molecular Dynamics (MD) simulations offer insights into biomolecular processes but generate complex, high-dimensional data.
  • Dimensionality reduction techniques like Principal Component Analysis (PCA), Time-Lagged Independent Component Analysis (TICA), and Self-Organising Maps (SOMs) aid in extracting functional dynamics information.
  • A user-friendly and flexible framework for SOM-based MD analysis is needed, with adaptable workflows and customizable options.

Purpose of the Study:

  • To design and develop SOMMD, an R package to streamline MD analysis workflows.
  • To facilitate the interpretation of atomistic trajectories using SOMs.
  • To provide tools for all stages of MD analysis, from data import to visualization.

Main Methods:

  • Development of SOMMD, an R package implementing Self-Organising Maps for MD data.
  • Integration of tools for importing diverse MD trajectory data types.
  • Implementation of enhanced visualization techniques.

Main Results:

  • SOMMD provides a streamlined workflow for MD data analysis using SOMs.
  • The package offers tools for data import, analysis, and visualization of atomistic trajectories.
  • Included example projects demonstrate applications in cluster analysis, pathway mapping, and transition network reconstruction.

Conclusions:

  • SOMMD enhances the interpretation of complex MD simulation data.
  • The R package offers a flexible and user-friendly approach to SOM-based analysis.
  • SOMMD supports various applications in understanding biomolecular dynamics.