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Updated: Jun 17, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Published on: January 16, 2019

A biclustering algorithm based on a bicluster enumeration tree: application to DNA microarray data.

Wassim Ayadi¹, Mourad Elloumi, Jin-Kao Hao

¹UTIC, Higher School of Sciences and Technologies of Tunis, 1008 Tunis, Tunisia. wassim.ayadi@gmail.com

|December 18, 2009

Summary

This summary is machine-generated.

We developed BiMine, a novel biclustering algorithm for DNA microarray analysis. BiMine effectively identifies biologically relevant gene expression patterns, outperforming existing methods.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

DNA microarray data analysis requires simultaneous clustering of genes and conditions.
Biclustering algorithms group rows (genes) and columns (conditions) with coherent patterns.
There is a need for more effective biclustering algorithms in genomics.

Purpose of the Study:

Introduce BiMine, a new enumeration algorithm for DNA microarray biclustering.
Address the need for improved biclustering methods in biological data analysis.

Main Methods:

BiMine utilizes a novel Average Spearman's rho (ASR) evaluation function.
Employs a Bicluster Enumeration Tree (BET) data structure for bicluster representation.
Incorporates a parametric rule to prune the search tree and avoid combinatorial explosion.

Main Results:

BiMine demonstrates competitive performance against other biclustering methods on synthetic and real DNA microarray data.
The algorithm successfully identifies biologically relevant biclusters, validated using a gene annotation web-tool.
Experimental results confirm BiMine's efficacy in discovering meaningful patterns in gene expression data.

Conclusions:

BiMine is an effective biclustering algorithm for DNA microarray data analysis.
The method offers a significant improvement in identifying biologically relevant gene expression patterns.
BiMine provides a valuable tool for researchers in genomics and bioinformatics.