gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data

Jiang, Haoliang; Zhou, Shuigeng; Guan, Jihong; Zheng, Ying

doi:10.1007/11691730_6

Cited by 30 publications

(31 citation statements)

References 6 publications

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“…Along with the algorithm, several metrics were also introduced to evaluate its performance. A revised and generalized version, called g-triCluster, was proposed the following year [21]. In this case, the novelty claimed was that more coherent triclusters could be found, as well as being robust to noise.…”

Section: Use Of Triclusteringmentioning

confidence: 99%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the available seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a Entropy 2015, 17 5001 geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen's self-organizing maps.

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Section: Use Of Triclusteringmentioning

confidence: 99%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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“…was published one year later [18]. The generalization claimed by the authors was based on the discovery of more coherent triclusters and on its robustness to noise.…”

Section: Related Workmentioning

confidence: 99%

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

et al. 2014

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Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.

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“…Triclustering Algorithms for Three-Dimensional Data Analysis 95:3 (4) Robustness: triclustering should be able to handle varying types (and degrees) of noise and missing values inherent to real-world 3D biomedical and social data (Jiang et al 2006). (5) Flexibility: the ability to discover a nonfixed number of triclusters with arbitrary size, shape, and positioning should be pursued to guarantee that all relevant subspaces are found.…”

Section: Introductionmentioning

confidence: 99%

Triclustering Algorithms for Three-Dimensional Data Analysis

Henriques

Madeira

2018

ACM Comput. Surv.

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Three-dimensional data are increasingly prevalent across biomedical and social domains. Notable examples are gene-sample-time, individual-feature-time, or node-node-time data, generally referred to as observationattribute-context data. The unsupervised analysis of three-dimensional data can be pursued to discover putative biological modules, disease progression profiles, and communities of individuals with coherent behavior, among other patterns of interest. It is thus key to enhance the understanding of complex biological, individual, and societal systems. In this context, although clustering can be applied to group observations, its relevance is limited since observations in three-dimensional data domains are typically only meaningfully correlated on subspaces of the overall space. Biclustering tackles this challenge but disregards the third dimension. In this scenario, triclustering-the discovery of coherent subspaces within three-dimensional data-has been largely researched to tackle these problems. Despite the diversity of contributions in this field, there still lacks a structured view on the major requirements of triclustering, desirable forms of homogeneity (including coherency, structure, quality, locality, and orthonormality criteria), and algorithmic approaches. This work formalizes the triclustering task and its scope, introduces a taxonomy to categorize the contributions in the field, provides a comprehensive comparison of state-of-the-art triclustering algorithms according to their behavior and output, and lists relevant real-world applications. Finally, it highlights challenges and opportunities to advance the field of triclustering and its applicability to complex three-dimensional data analysis.

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gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data

Cited by 30 publications

References 6 publications

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

Triclustering Algorithms for Three-Dimensional Data Analysis

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