Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Alzahrani, Majed; Kuwahara, Hiroyuki; Wang, Wei; Gao, Xin

doi:10.1093/bioinformatics/btx199

Cited by 8 publications

(5 citation statements)

References 56 publications

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“…However, their use depends on the choice of color. In both methods, a shuffling of rows/columns could be needed for an improved visualization [ 69 , 161 , 162 ].…”

Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

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“…However, their use depends on the choice of color. In both methods, a shuffling of rows/columns could be needed for an improved visualization [ 69 , 161 , 162 ].…”

Section: Evaluation Measuresmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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“…The biclustering algorithm was first proposed in Hartigan (1972), is a two-dimensional data mining technique that allows simultaneous clustering of rows (representing genes) and columns (representing samples/conditions) in a gene expression matrix. Developments continued in the following decades, with (Cheng and Church, 2000;Lazzeroni and Owen, 2000;Bergmann et al, 2003;Kluger et al, 2003;Chiu et al, 2004;Prelić et al, 2006;Dhollander et al, 2007;Gu and Liu, 2008;Li et al, 2009;Hochreiter et al, 2010;Madeira et al, 2010;Medina et al, 2010;Chen et al, 2011;De Smet and Marchal, 2011;Zhao et al, 2011;Zhou et al, 2012;Goncalves and Madeira, 2014;Henriques and Madeira, 2016a,b;Alzahrani et al, 2017;Guo et al, 2021) being articles on different clustering algorithms. Among them, BCPlaid (Lazzeroni and Owen, 2000), QUBIC (Li et al, 2009), C&C (Cheng and Church, 2000), FABIA (Hochreiter et al, 2010) are the more popular biclustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods

Xuan

Yin²,

Peng³

2023

Front. Microbiol.

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Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.

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“…However, there are two types of patterns that show special interest in nature: shifting and scaling patterns [14]. Most of the approaches for shifting pattern induction are based on a measure named Mean Square Residue (MSR) [4] and its variants [15][16][17][18], or on graph theory [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Shifting Pattern Biclustering and Boolean Reasoning Symmetry

Michalak,

Aguilar-Ruiz

2023

Symmetry

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There are several goals of the two-dimensional data analysis: one may be interested in searching for groups of similar objects (clustering), another one may be focused on searching for some dependencies between a specified one and other variables (classification, regression, associate rules induction), and finally, some may be interested in serching for well-defined patterns in the data called biclusters. It was already proved that there exists a mathematically proven symmetry between some patterns in the matrix and implicants of data-defined Boolean function. This paper provides the new look for a specific pattern search—the pattern named the δ-shifting pattern. The shifting pattern is interesting, as it accounts for constant fluctuations in data, i.e., it captures situations in which all the values in the pattern move up or down for one dimension, maintaining the range amplitude for all the dimensions. Such a behavior is very common in real data, e.g., in the analysis of gene expression data. In such a domain, a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. A δ-shifting pattern meets the necessity of shifting pattern induction together with the bias of the real values acquisition where the original shifts may be disturbed with some outer conditions. Experiments with a real dataset show the potential of our approach at finding biclusters with δ-shifting patterns, providing excellent performance. It was possible to find the 12×9 pattern in the 112×9 input data with MSR=0.00653. The experiments also revealed that δ-shifting patterns are quite difficult to be found by some well-known methods of biclustering, as these are not designed to focus on shifting patterns—results comparable due to MSR had much more variability (in terms of δ) than patterns found with Boolean reasoning.

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Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Cited by 8 publications

References 56 publications

Biclustering data analysis: a comprehensive survey

Biclustering data analysis: a comprehensive survey

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods

Shifting Pattern Biclustering and Boolean Reasoning Symmetry

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