Biclustering on expression data: A review

Abstract

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.

ClinMine: Optimizing the Management of Patients in Hospital

“…Several types of biclusters exist (similar values, similar row values, similar column values) [Pontes15], and biclustering algorithms are usually designed to detect a subset of these existing types. This design choice depends on the data, context and application.…”

“…Although many biclustering methods have been proposed since the seminal work of Cheng and Church on the subject [Cheng00], the overwhelming majority of those focus on biological data analysis: detection of gene expression, protein interaction, microarray data analysis, etc. Therefore, most biclustering algorithms are designed to handle only one type of data, either numeric or binary (see [Pontes15] for a recent review on the subject). One algorithm, SAMBA (Statistical Algorithmic Method for Bicluster Analysis) [Tanay04], deals with heterogeneous data by integrating data from different sources.…”

Biclustering: An application of Dual Topic Models

“…Depending on the application, different types of biclusters have been studied. For example, a bicluster whose values are the same is known as the constant bicluster type [1].…”

QtBiVis: a software toolbox for visual analysis of biclustering experiment

“…Biclustering algorithms have been applied to identify groups of genes that show resemblance under particular subsection of conditions. Multiple biclustering methods have been developed so far [2], [3], [4]. Different metrics have been adapted to measure gene expression level [5], [6].…”