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G-bic: generating synthetic benchmarks for biclustering.

Eduardo N Castanho1, João P Lobo2, Rui Henriques3

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 016, 1749-016, Lisbon, Portugal. ejcastanho@ciencias.ulisboa.pt.

BMC Bioinformatics
|December 5, 2023
PubMed
Summary

G-Bic generates synthetic datasets for robust biclustering algorithm assessment. This tool ensures reproducible, representative, and realistic benchmarks for evaluating biclustering performance across diverse data types.

Keywords:
BiclusteringSynthetic data generatorTime series analysisUnsupervised learning

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Area of Science:

  • Computational Biology
  • Data Mining
  • Bioinformatics

Background:

  • Biclustering is vital for analyzing complex data in biomedicine, recommendations, and text mining.
  • Evaluating biclustering algorithms requires ground truth, which real datasets lack.
  • Synthetic data generation is crucial for reproducible and representative algorithm assessment.

Purpose of the Study:

  • Introduce G-Bic, a novel synthetic dataset generator for biclustering.
  • Provide a tool for normative assessment of biclustering algorithms.
  • Address challenges in generating realistic and reproducible synthetic data.

Main Methods:

  • G-Bic generates synthetic datasets with controllable pattern coherence, data quality, and positioning.
  • The generator accommodates mixed-type and time-series data.
  • Default configurations are provided for reproducible benchmark generation.

Main Results:

  • G-Bic produces synthetic benchmarks for evaluating biclustering algorithms.
  • The tool replicates real data regularities across diverse domains.
  • Empirical strategies for simulating real data properties are discussed.

Conclusions:

  • G-Bic offers a robust, parametrizable solution for biclustering analysis.
  • It enables reliable assessment of biclustering solutions using internal and external metrics.
  • Facilitates standardized comparison of biclustering algorithm performance.