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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Integrating machine learning techniques into robust data enrichment approach and its application to gene expression

Utku Erdoğdu1, Mehmet Tan2, Reda Alhajj3

  • 1Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey. utku@ceng.metu.edu.tr

International Journal of Data Mining and Bioinformatics
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Summary

Generating more gene expression data samples is crucial for research. This study presents a novel machine learning framework using multiple models to automate sample generation, enhancing data availability for analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Limited sample sizes in gene expression data analysis hinder effective knowledge discovery and model training.
  • Existing data analysis approaches often suffer from insufficient data for robust model development.

Purpose of the Study:

  • To develop and validate an automated framework for generating synthetic gene expression data samples.
  • To address the challenge of data scarcity in gene expression analysis through sophisticated machine learning techniques.

Main Methods:

  • A multi-model framework integrating Probabilistic Boolean Networks (PBN), Hierarchical Markov Models (HIMM), and genetic algorithms.
  • Each model independently learns domain characteristics from existing data to generate new samples.
  • Ensuring model independence to prevent bias and skewed data generation.

Main Results:

  • The framework successfully generated new gene expression data samples, complementing existing datasets.
  • Extensive testing demonstrated the effectiveness and applicability of the multi-model approach.
  • The generated samples showed promise in enhancing the available data for analysis.

Conclusions:

  • The proposed multi-model framework offers a viable solution for automating gene expression data sample generation.
  • This approach can significantly improve the availability of data for training and testing analytical models.
  • The framework's ability to generate diverse and unbiased samples is crucial for advancing gene expression research.