Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene expression data analysis is crucial for identifying biological relationships.
• Existing methods for analyzing vast biological datasets require innovative and reliable approaches.
• Evolutionary algorithms show promise but need further refinement for biological data analysis.

Purpose of the Study:

• To introduce the Online-Adjusted EVOlutionary Biclustering algorithm (OAEVOB) for efficient analysis of large gene expression datasets.
• To enhance the identification of significant gene groups by dynamically adjusting evolutionary parameters.
• To evaluate OAEVOB's performance and robustness across diverse sequencing data sources.

Main Methods:

• Developed OAEVOB, an evolutionary biclustering algorithm with an online-adjustment feature.
• Utilized Pearson correlation, distance correlation, biweight midcorrelation, and mutual information for similarity assessment.
• Tested OAEVOB on six diverse gene expression datasets (DNA microarray, RNA sequencing, single-cell RNA sequencing).

Main Results:

• OAEVOB identified significant gene expression biclusters with correlations > 0.5 across all similarity measures.
• Functional enrichment analysis revealed cancer and tissue-specific biological functions within identified biclusters.
• OAEVOB demonstrated superior performance compared to state-of-the-art methods, showing robustness to noise and data variations.

Conclusions:

• OAEVOB is an effective and robust algorithm for analyzing diverse and large-scale gene expression data.
• The algorithm successfully identifies biologically relevant gene biclusters, including those related to cancer and specific tissues.
• OAEVOB offers an improved approach for gene expression data analysis, outperforming existing techniques.