A biclustering algorithm for extracting bit-patterns from binary datasets

Rodríguez–Baena, Domingo S.; Pérez-Pulido, Antonio J.; Aguilar–Ruiz, Jesús S.

doi:10.1093/bioinformatics/btr464

Cited by 77 publications

(41 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They usually try to avoid an exponential running time by restricting the size of the searched biclusters. We used three exhaustive enumeration algorithms in this paper: Statistical-Algorithmic Method for Bicluster Analysis (SAMBA) [6], a procedure that models the input dataset as a bipartite graph, where one set of nodes corresponds to the genes and the other is related to the experimental conditions, and finds complete bipartite subgraphs composed of gene nodes with bounded degree; Bit-Pattern Biclustering Algorithm (BiBit) [31], which searches for maximal biclusters in binary datasets by applying the logical AND operator over all possible gene pairs; and Differentially Expressed Biclusters (DeBi) [32], an algorithm based on a frequent itemset approach that applies a depth-first traversal on an enumeration tree to discover hidden patterns in data.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, for each level l , a new data matrix is generated with elements equal to 1 where the expression value level is greater than or equal to l and 0 otherwise. In our experiments with real data, BiBit was run only on the matrix of the highest level after preprocessing step, since the biclusters found at this level would be more specialized [31] because the gene expression values contained in them would be the most upregulated ones.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A systematic comparative evaluation of biclustering techniques

2017

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A systematic comparative evaluation of biclustering techniques

2017

View full text Add to dashboard Cite

show abstract

“…The other consists of not only TOP 1 but also TOP 2 and 3 columns. Bimax [11], BiBit [12], PDNS [8], BISES [5] and BISERS are applied to the former matrix, which requires no exclusive selection of a column. BISES and BISERS are applied to the latter matrix.…”

Section: Comparison With Other Biclustering Methodsmentioning

confidence: 99%

A New Biclustering Algorithm with Exclusive Random Selection of Columns for Predicting Recognition Spots on Protein Molecular Surfaces

Nishimura¹,

Ohkawa²

2018

IJBBB

View full text Add to dashboard Cite

Abstract:A protein and a ligand have a process of recognizing each other when they are remote before approaching for binding. In this process, there should be a local portion corresponding to a target of the recognition (a recognition spot) on the protein molecular surface. In this paper, we proposed a new biclustering algorithm BISERS for predicting recognition spots on protein molecular surfaces, in which a portion of the molecular surface of a query protein that frequently shows the similarity to other specific proteins binding to the similar ligand is extracted by biclustering. In BISERS, the similarity between rows as well as the similarity between columns is introduced into the evaluation function for updating the biclusters. In addition, the random sampling is applied to the process of exclusive selection of the column for reducing the computational cost effectively. Experimental results for 60 proteins show the effectiveness of our BISERS algorithm.

show abstract

“…Several biclustering algorithms which are specifically designed for binary datasets [21,47] or time series gene expression data [48,49] can be found in the literature. In the case of algorithms for binary datasets, a discretization step is necessary before the algorithm is applied, and therefore, a preprocessed matrix is used instead of the original gene expression data matrix.…”

Section: Related Researchmentioning

confidence: 99%