BicFinder: a biclustering algorithm for microarray data analysis

Ayadi, Wassim; Elloumi, Mourad; Hao, Jin‐Kao

doi:10.1007/s10115-011-0383-7

Cited by 47 publications

(15 citation statements)

References 41 publications

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“…To analyze each of the obtained enrichments we used two different approaches. The first one is the same used in [15, 17, 27, 32, 55, 56] which is based on the percentage of enriched biclusters among the biclusters found by each algorithm in each dataset. The second approach is the same used in [8] which, for two lists of gene clusters denoted by r 1 and r 2 , counts the number of times that r 1 presented clusters of genes with better p -values than r 2 and vice-versa, combining such quantities by means of Eq.…”

Section: Resultsmentioning

confidence: 99%

A systematic comparative evaluation of biclustering techniques

2017

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BackgroundBiclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets.ResultsWe conducted a broader comparative study involving seventeen algorithms, which were run on three synthetic data collections and two real data collections with a more representative number of datasets. For the experiments with synthetic data, five different experimental scenarios were studied: different levels of noise, different numbers of implanted biclusters, different levels of symmetric bicluster overlap, different levels of asymmetric bicluster overlap and different bicluster sizes, for which the results were assessed with more suitable external measures. For the experiments with real datasets, the results were assessed by gene set enrichment and clustering accuracy.ConclusionsWe observed that each algorithm achieved satisfactory results in part of the biclustering tasks in which they were investigated. The choice of the best algorithm for some application thus depends on the task at hand and the types of patterns that one wants to detect.

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Section: Resultsmentioning

confidence: 99%

A systematic comparative evaluation of biclustering techniques

2017

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“…The different approaches for co-clustering (see surveys by [31,34,40]) are based on various models: additive vs. multiplicity, axis alignment, rows over columns preferment, cluster scoring function, overlapping, etc. ; and, algorithmic strategies: greedy [3,9], kernel based [29,36,47], exhaustive enumeration [39], spectral analysis [27], CTWC [15], Bayesian networks [6], etc. Substantial effort has been directed at non-lagged co-clustering of datasets with temporal nature [4,21,22,35, inter alia], surveyed by [38], utilizing time as a natural ordering on the columns.…”

Section: Related Workmentioning

confidence: 99%

Sleeved co-clustering of lagged data

2011

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The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a submatrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real-life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow, and navigation, but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for mining lagged co-clusters. We prove that, with fixed probability, the algorithm mines a lagged co-cluster which encompasses the optimal lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values, and overlapping patterns. The algorithm is extensively evaluated using both artificial and real-world test environments. The first enable the evaluation of specific, isolated properties of the algorithm. The latter (river flow and topographic data) enable the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data and non-temporal.

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“…For the synthetic data, we compare BiMine+ results with the results of the original BiMine [6], BILS [4] and BicFinder [5], and some prominent biclustering algorithms used by the community, namely, CC [17], OPSM [9], ISA [10] and Bimax [44]. For these reference algorithms, we have used Biclustering Analysis Toolbox (BicAT), a recent software platform for clustering-based data analysis that integrates all these biclustering algorithms [8].…”

Section: Experimental Studiesmentioning

confidence: 99%