Bi-Force

Bi-Force is a software tool based on the weighted bicluster editing model. Given any pairwise similarities, this tool is designed to perform biclustering on arbitrary sets of biological entities. Biclustering, also known as 'simultaneous clustering' or 'co-clustering', is a popular approach used in bioinformatics to discover local patterns in gene expression data and similar biomedical data types. Bi-Force has been successfully utilized to analyze high-dimensional biological data on a large scale, and it has demonstrated excellent performance in discovering local patterns in gene expression data and similar biomedical data types.

To evaluate the power of Bi-Force, the authors compared it with two existing algorithms in the BiCluE software package. Then we followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A comparative analysis of biclustering algorithms for gene expression data. Brief. Bioinform., 14:279-292.) and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, BiMax, Spectral, xMOTIFs, and ISA. To this end, a suite of synthetic datasets and nine large gene expression datasets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance.

The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. Therefore, Bi-Force has outperformed existing tools, at least when following the evaluation protocols from Eren et al. This makes Bi-Force a valuable tool for biologists and bioinformaticians who must analyze high-dimensional biological data on a large scale.