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Mosclust: a software library for discovering significant structures in bio-molecular data.

Giorgio Valentini1

  • 1DSI, Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, Via Comelico 39, Italy. valentini@dsi.unimi.it

Bioinformatics (Oxford, England)
|November 28, 2006
PubMed
Summary

The mosclust R package uses stability-based algorithms to find significant structures in bio-molecular data. It offers stability indices and statistical tests for robust clustering and multi-level structure discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • The R package mosclust is introduced for model order selection in clustering.
  • It addresses the challenge of discovering significant structures within complex bio-molecular datasets.

Purpose of the Study:

  • To implement stability-based algorithms for robust clustering.
  • To provide tools for assessing the significance of discovered data structures.

Main Methods:

  • Utilizes the concept of stability for structure discovery.
  • Employs various data perturbation methods including resampling, random projections, and noise injection to compute stability indices.
  • Integrates statistical tests to evaluate the significance of multi-level structures.

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Main Results:

  • The software library provides stability indices derived from diverse perturbation techniques.
  • Statistical tests are available to validate the significance of identified structures.
  • Facilitates the discovery of meaningful patterns in bio-molecular data.

Conclusions:

  • The mosclust package offers a robust framework for clustering bio-molecular data.
  • It enhances the reliability of structure discovery through stability assessment and statistical validation.
  • A valuable tool for researchers in bioinformatics and computational biology.