Dna Microarray Data Analysis: A New Survey on Biclustering

Saber, Haifa Ben; Elloumi, Mourad

doi:10.34040/ijcb.4.1.2014.36

Cited by 21 publications

(4 citation statements)

References 56 publications

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“…The biclustering problem can be formulated as follows: Given a data matrix M , construct a group of biclusters B opt associated with M such that: where f is an objective function measuring the quality , i.e., degree of coherence, of a group of biclusters and BC ( M ) is the set of all the possible groups of biclusters associated with M [ 15 , 16 ]. Biclustering is an NP-hard problem [ 4 , 17 ].…”

Section: Biclustering Of Gene Expression Datamentioning

confidence: 99%

An evaluation study of biclusters visualization techniques of gene expression data

Aouabed

Elloumi

Santamaría

2021

Journal of Integrative Bioinformatics

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Biclustering is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that have similar behavior under subgroups of conditions. The classified genes can have independent behavior under other subgroups of conditions. Discovering such co-expressed genes, called biclusters, can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: bi-dimensionality which means grouping both genes and conditions simultaneously and overlapping which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms, which continue to be developed at a constant pace, give as output a large number of overlapping biclusters. Visualizing groups of biclusters is still a non-trivial task due to their overlapping. In this paper, we present the most interesting techniques to visualize groups of biclusters and evaluate them.

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Section: Biclustering Of Gene Expression Datamentioning

confidence: 99%

An evaluation study of biclusters visualization techniques of gene expression data

Aouabed

Elloumi

Santamaría

2021

Journal of Integrative Bioinformatics

Self Cite

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“…Co-regulated gene module detection will assist us in identifying its biological functions or molecular pathways. Conventional clustering methods uncover co-expressed genomic profiles across all samples; however, they cannot detect shared patterns in a subset of genes and among a subset of samples, called co-clusters or biclusters[ 1 ]. The biclustering algorithm, first introduced in 2000 by Cheng and Church (CC) [ 2 ], was designed to discover gene modules among a subset of samples.…”

Section: Introductionmentioning

confidence: 99%

Bi-EB: Empirical Bayesian Biclustering for Multi-Omics Data Integration Pattern Identification among Species

Yazdanparast

Zhang

et al. 2022

Genes

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Although several biclustering algorithms have been studied, few are used for cross-pattern identification across species using multi-omics data mining. A fast empirical Bayesian biclustering (Bi-EB) algorithm is developed to detect the patterns shared from both integrated omics data and between species. The Bi-EB algorithm addresses the clinical critical translational question using the bioinformatics strategy, which addresses how modules of genotype variation associated with phenotype from cancer cell screening data can be identified and how these findings can be directly translated to a cancer patient subpopulation. Empirical Bayesian probabilistic interpretation and ratio strategy are proposed in Bi-EB for the first time to detect the pairwise regulation patterns among species and variations in multiple omics on a gene level, such as proteins and mRNA. An expectation–maximization (EM) optimal algorithm is used to extract the foreground co-current variations out of its background noise data by adjusting parameters with bicluster membership probability threshold Ac; and the bicluster average probability p. Three simulation experiments and two real biology mRNA and protein data analyses conducted on the well-known Cancer Genomics Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE) verify that the proposed Bi-EB algorithm can significantly improve the clustering recovery and relevance accuracy, outperforming the other seven biclustering methods—Cheng and Church (CC), xMOTIFs, BiMax, Plaid, Spectral, FABIA, and QUBIC—with a recovery score of 0.98 and a relevance score of 0.99. At the same time, the Bi-EB algorithm is used to determine shared the causality patterns of mRNA to the protein between patients and cancer cells in TCGA and CCLE breast cancer. The clinically well-known treatment target protein module estrogen receptor (ER), ER (p118), AR, BCL2, cyclin E1, and IGFBP2 are identified in accordance with their mRNA expression variations in the luminal-like subtype. Ten genes, including CCNB1, CDH1, KDR, RAB25, PRKCA, etc., found which can maintain the high accordance of mRNA–protein for both breast cancer patients and cell lines in basal-like subtypes for the first time. Bi-EB provides a useful biclustering analysis tool to discover the cross patterns hidden both in multiple data matrixes (omics) and species. The implementation of the Bi-EB method in the clinical setting will have a direct impact on administrating translational research based on the cancer cell screening guidance.

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“…With the emergence of advanced information technology and its increasing applications in electronic communications, a wide variety of modern devices have been designed based on big data whose dimension is exceptionally high [13, 14, 15]. The noise and redundancy, including the irrelevant information, come from a large number of complex parameters in the model, and it results in a limited ability to generalize the learning model and impose inefficient requirements such as more memory usage and longer processing time on the computational devices since the corresponding algorithms suffer enormously from over-fitting [16, 17, 18, 19, 20, 21].…”

Section: Introductionmentioning

confidence: 99%

High Dimensionality Reduction by Matrix Factorization for Systems Pharmacology

Mehrpooya

Saberi-Movahed

Azizizadeh

et al. 2021

Preprint

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The extraction of predictive features from the complex high-dimensional multi-omic data is neces- sary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clin- ical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the Matrix Factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis. We have applied these techniques as well as three other matrix factorization methods to analyze eight different gene ex- pression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a Tyrosine Kinase Inhibitor (TKI) treatment response in the Cancer Cell Line Encyclopedia (CCLE).

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Dna Microarray Data Analysis: A New Survey on Biclustering

Cited by 21 publications

References 56 publications

An evaluation study of biclusters visualization techniques of gene expression data

An evaluation study of biclusters visualization techniques of gene expression data

Bi-EB: Empirical Bayesian Biclustering for Multi-Omics Data Integration Pattern Identification among Species

High Dimensionality Reduction by Matrix Factorization for Systems Pharmacology

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