On biclustering of gene expression data

Mounir, Mahmoud; Hamdy, Mohamed

doi:10.1109/intelcis.2015.7397290

Cited by 7 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subset of rows of H with high values in the same column of W represents a centroid. The set of genes and conditions with relatively high values in the same column of W and the corresponding rows of H is called bicluster [69]. Finally, whether some entries in a row of H have coefficients greater than zero, the conditions are influenced by local characters in the data.…”

Section: If the Matrix Euclidean Normmentioning

confidence: 99%

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

2021

View full text Add to dashboard Cite

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.

show abstract

Section: If the Matrix Euclidean Normmentioning

confidence: 99%

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

2021

View full text Add to dashboard Cite

show abstract

“…According to Sim et al, the main difference with subspace clustering is that these approaches consider the objects and the features of the data set interchangeably, and cluster simultaneously objects and features exhibiting common patterns. Different survey articles have been dedicated to these paradigms, we refer the reader to Charrad and Ahmed, Sim et al, and Mounir and Hamdy for a detailed presentation.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary subspace clustering using variable genome length

Peignier

Rigotti

Beslon

2019

Computational Intelligence

View full text Add to dashboard Cite

Subspace clustering is a data‐mining task that groups similar data objects and at the same time searches the subspaces where similarities appear. For this reason, subspace clustering is recognized as more general and complicated than standard clustering. In this article, we present ChameleoClust+, a bioinspired evolutionary subspace clustering algorithm that takes advantage of an evolvable genome structure to detect various numbers of clusters located in different subspaces. ChameleoClust+ incorporates several biolike features such as a variable genome length, both functional and nonfunctional elements, and mutation operators including large rearrangements. It was assessed and compared with the state‐of‐the‐art methods on a reference benchmark using both real‐world and synthetic data sets. Although other algorithms may need complex parameter settings, ChameleoClust+ needs to set only one subspace clustering ad hoc and intuitive parameter: the maximal number of clusters. The remaining parameters of ChameleoClust+ are related to the evolution strategy (eg, population size, mutation rate), and a single setting for all of them turned out to be effective for all the benchmark data sets. A sensitivity analysis has also been carried out to study the impact of each parameter on the subspace clustering quality.

show abstract