A multi-GPU biclustering algorithm for binary datasets

Fernández, Aurelio López; Rodríguez–Baena, Domingo S.; Gómez-Vela, Francisco; García-Torres, Miguel

doi:10.1016/j.jpdc.2020.09.009

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…These data suggested that the contribution of EOGT and LFNG to Notch signaling is qualitatively different, possibly through the differential impact on multiple Notch-ligand pairs, which leads to the transactivation of distinct sets of Notch signaling target genes, including HES1 and HEY1 [ 35 ]. Further in-depth bioinformatics analyses included Biclustering methods, will help elucidate the pathological relevance of the observed correlations in tumor progression [ 36 , 37 , 38 , 39 ].…”

Section: Resultsmentioning

confidence: 99%

Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers

et al. 2021

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Notch signaling receptors, ligands, and their downstream target genes are dysregulated in pancreatic ductal adenocarcinoma (PDAC), suggesting a role of Notch signaling in pancreatic tumor development and progression. However, dysregulation of Notch signaling by post-translational modification of Notch receptors remains poorly understood. Here, we analyzed the Notch-modifying glycosyltransferase involved in the regulation of the ligand-dependent Notch signaling pathway. Bioinformatic analysis revealed that the expression of epidermal growth factor (EGF) domain-specific O-linked N-acetylglucosamine (EOGT) and Lunatic fringe (LFNG) positively correlates with a subset of Notch signaling genes in PDAC. The lack of EOGT or LFNG expression inhibited the proliferation and migration of Panc-1 cells, as observed by the inhibition of Notch activation. EOGT expression is significantly increased in the basal subtype, and low expression of both EOGT and LFNG predicts better overall survival in PDAC patients. These results imply potential roles for EOGT- and LFNG-dependent Notch signaling in PDAC.

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Section: Resultsmentioning

confidence: 99%

Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers

et al. 2021

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“…DNA microarray technologies help to measure levels of expression in experimental circumstances of thousands of genes [ 1 ]. Local patterns have motivated the large study to use pattern-based searches to deal with them.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Healthcare Biclustering‐Based Prediction on Gene Expression Dataset

Ramkumar¹,

Basker²,

Duraisamy³

et al. 2022

BioMed Research International

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In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

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“…We discussed in detail the parallelization of NMF (see Algorithm 6) in a GPU through the CUDA library in line with NMF+HC [57,78,79]. However, as we mentioned in Section 1, a small disturbance in the data or a poor decision determined in the initial steps of the algorithm can profoundly modify the final results.…”

Section: Strategies For Parallelizing Nmf Algorithmmentioning

confidence: 99%

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

2021

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Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.

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A multi-GPU biclustering algorithm for binary datasets

Cited by 10 publications

References 34 publications

Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers

Bioinformatics and Functional Analyses Implicate Potential Roles for EOGT and L-fringe in Pancreatic Cancers

[Retracted] Healthcare Biclustering‐Based Prediction on Gene Expression Dataset

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

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