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Related Experiment Video

Updated: Dec 26, 2025

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression Data.

Koyel Mandal, Rosy Sarmah, Dhruba Kumar Bhattacharyya

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |March 17, 2020
    PubMed
    Summary

    This study introduces a new algorithm, Pathway-based Order Preserving Biclustering (POPBic), to find gene groups with similar expression patterns. POPBic effectively identifies biologically significant biclusters in cancer data, even with noisy expression patterns.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Understanding gene expression patterns is crucial for uncovering biological mechanisms.
    • Biclustering algorithms are vital for analyzing large-scale gene expression data.
    • Integrating biological knowledge enhances traditional biclustering.

    Purpose of the Study:

    • To propose a novel Pathway-based Order Preserving Biclustering (POPBic) algorithm.
    • To incorporate biological pathway information (KEGG) into biclustering.
    • To identify groups of genes with similar expression patterns and shared biological pathways.

    Main Methods:

    • The POPBic algorithm utilizes the Longest Common Subsequence concept for gene pairs with common pathways.
    • It involves two main steps: selection of significant seed genes and extraction of biclusters.
    • Experiments were conducted using synthetic datasets and four cancer microarray gene expression datasets.

    Main Results:

    • POPBic demonstrates robustness against noise in gene expression data.
    • The algorithm successfully identifies overlapping biclusters.
    • Biologically significant biclusters were discovered in cancer gene expression datasets.

    Conclusions:

    • POPBic effectively integrates pathway information to improve biclustering.
    • The algorithm performs consistently well compared to existing methods.
    • POPBic offers a valuable tool for analyzing gene expression data in cancer research.