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Effective gene expression data generation framework based on multi-model approach.

Utku Sirin1, Utku Erdogdu2, Faruk Polat2

  • 1School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Route Cantonale, 1015 Lausanne, Switzerland.

Artificial Intelligence in Medicine
|July 20, 2016
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Summary
This summary is machine-generated.

This study introduces a novel framework to generate artificial gene expression data, overcoming sample size limitations. The multi-model approach enhances data quality, improving gene regulatory network inference.

Keywords:
Gene expression data generationGene regulation network modelingGenetic algorithmHierarchical Markov modelsMulti-model approachOrdinary differential equationsProbabilistic Boolean networks

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression datasets often suffer from a limited number of samples relative to the number of genes.
  • This scarcity poses significant challenges for computational methods reliant on sufficient data for accurate analysis.
  • Gene regulatory network (GRN) inference is particularly sensitive to sample size limitations.

Purpose of the Study:

  • To develop a multi-model framework for generating artificial gene expression data to address sample size limitations.
  • To improve the quality and utility of gene expression data for computational analysis and GRN inference.
  • To integrate diverse GRN models into a unified data generation process.

Main Methods:

  • A multi-model framework was developed, integrating data from four distinct GRN models: ordinary differential equations, probabilistic Boolean networks, multi-objective genetic algorithm, and hierarchical Markov models.
  • Gene expression samples were generated separately from each GRN model.
  • A multi-objective selection method was employed to pool and select the best samples based on compatibility, diversity, and coverage.

Main Results:

  • The multi-objective sample selection mechanism achieved high compatibility (up to 95%), diversity (up to 10%), and coverage (up to 50%).
  • Generated samples demonstrated superior quality compared to individual models, with up to 1.5x higher compatibility, 2x higher diversity, and 2x higher coverage.
  • GRNs inferred from the framework's data showed significant improvements: 2.4x higher precision, 12x higher recall, and 5.4x higher f-measure compared to those from original samples.

Conclusions:

  • The proposed unified framework effectively enhances the quality of generated gene expression samples by integrating diverse computational models.
  • This approach overcomes the challenges of limited sample sizes without requiring deep understanding of individual model intricacies.
  • The generated artificial data captures biological relationships potentially missed in original datasets, offering a richer resource for biological discovery.