Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.
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- Traditional methods for analyzing gene relationships often focus on symmetric correlations; however, this study introduces a technique to detect 'boolean-asymmetric' relationships (BARs) in gene expression data.
- The proposed method identifies biclusters enriched with these asymmetric relationships, revealing regulatory influences of certain genes on others that are not apparent in typical analyses.
- When applied to single-cell RNA sequencing data, the BAR-biclusters provided new insights regarding different cell subsets and gene pathways, leading to improved predictive models compared to those using only symmetric data.
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