DPBC: Distance Based Possibilistic Biclustering with Application to Gene Expression Analysis

Mahfouz, Mohamed A.; Ismail, Mohamed A.

doi:10.1109/icbbe.2009.5162662

Cited by 5 publications

(9 citation statements)

References 7 publications

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“…de Souto et al [34] compare three normalization procedures (z-score, scaling and rank-based procedures) over gene expression datasets using alternative clustering algorithms. Additional methods for preprocessing the input matrix have been reported [118,83,25]. levels of noise allowance, commonly occurring in binarized partitions or through the use of rule-based approaches under a relaxed level of confidence.…”

Section: Closing Options: Postprocessing Biclustering Solutionsmentioning

confidence: 99%

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A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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Section: Closing Options: Postprocessing Biclustering Solutionsmentioning

confidence: 99%

“…A sensitivity analysis on the impact of the number of items on the quality and size of biclusters was, first, performed in Bidens [83] and BiModule [96]. Fig.…”

Section: Mapping Options: Preprocessing Input Datamentioning

confidence: 99%

A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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“…Normalization techniques are often required to enhance differences across rows and/or columns. Alternative methods have been reported [ 34 , 48 ]. BicPAM allows the normalization criteria to be applied in the context of a row, a column or the overall matrix.…”

Section: Bicpam: Pattern-based Biclusteringmentioning

confidence: 99%

“…GenMiner [ 15 ] includes external knowledge within the input matrix to derive biclusters from association rules that relate annotations (external grouping of rows or columns) with computed clusters of rows and columns from (closed) frequent patterns using CLOSE [ 33 ]. Although other biclustering approaches seize contributions from these previous works [ 34 , 35 ], we do not refer to them as PM-based appproaches if the core mining task does not rely on FIM.…”

Section: Introductionmentioning

confidence: 99%

BicPAM: Pattern-based biclustering for biomedical data analysis

Henriques

Madeira

2014

Algorithms Mol Biol

124

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BackgroundBiclustering, the discovery of sets of objects with a coherent pattern across a subset of conditions, is a critical task to study a wide-set of biomedical problems, where molecular units or patients are meaningfully related with a set of properties. The challenging combinatorial nature of this task led to the development of approaches with restrictions on the allowed type, number and quality of biclusters. Contrasting, recent biclustering approaches relying on pattern mining methods can exhaustively discover flexible structures of robust biclusters. However, these approaches are only prepared to discover constant biclusters and their underlying contributions remain dispersed.MethodsThe proposed BicPAM biclustering approach integrates existing principles made available by state-of-the-art pattern-based approaches with two new contributions. First, BicPAM is the first efficient attempt to exhaustively mine non-constant types of biclusters, including additive and multiplicative coherencies in the presence or absence of symmetries. Second, BicPAM provides strategies to effectively compose different biclustering structures and to handle arbitrary levels of noise inherent to data and with discretization procedures.ResultsResults show BicPAM’s superiority against its peers and its ability to retrieve unique types of biclusters of interest, to efficiently deliver exhaustive solutions and to successfully recover planted biclusters in datasets with varying levels of missing values and noise. Its application over gene expression data leads to unique solutions with heightened biological relevance.ConclusionsBicPAM approaches integrate existing disperse efforts towards pattern-based biclustering and provides the first critical strategies to efficiently discover exhaustive solutions of biclusters with shifting, scaling and symmetric assumptions with varying quality and underlying structures. Additionally, BicPAM dynamically adapts its behavior to mine data with different levels of missing values and noise.

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“…For instance, when missing values are present, classical data mining algorithms discover multiple small fragments of the real cluster, but miss the cluster itself [15]. The problem is even worse for clusters which cover many rows and columns, since they are more vulnerable to this kind of "noise" [17].…”

Section: Introductionmentioning

confidence: 99%

Abba

Colantonio

Pietro

Ocello

et al. 2010

Proceedings of the 2010 ACM Symposium on Applied Computing

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Missing values frequently pose problems in binary matrices analysis since they can hinder downstream analysis of the datasets. Despite the presence of many imputation methods that have been developed to substitute missing values with estimated values, these available techniques have some common disadvantages: they need to fix some parameters (e.g., number of patterns, number of rows to consider) to estimate missing values-with little theoretical support to determine these parameters-; and, missing values need to be recomputed from scratch as parameters change.In this paper we propose a novel algorithm (AB B A: Adaptive Bicluster-Based Approach) that does not have the above limitations. Further, a formal framework that justifies the rationales behind AB B A is detailed. Finally, experimental results over both synthetic and real data confirm the viability of our approach and the quality of the results, that overcomes the ones achieved by the main competing algorithm (KNN).

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DPBC: Distance Based Possibilistic Biclustering with Application to Gene Expression Analysis

Cited by 5 publications

References 7 publications

A structured view on pattern mining-based biclustering

A structured view on pattern mining-based biclustering

BicPAM: Pattern-based biclustering for biomedical data analysis

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