Graph-based unsupervised feature selection and multiview clustering for microarray data

Swarnkar, Tripti; Mitra, Pabitra

doi:10.1007/s12038-015-9559-8

Cited by 12 publications

(6 citation statements)

References 39 publications

(46 reference statements)

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“…A good survey on existing research on the development of multi-view clustering algorithms can be found in [13,28]. On the problem of gene selection, a graph-theoretic multi-view clustering on gene expression data was proposed in [29]. Though their proposed algorithm is a multi-view approach, the views are developed based on expression data, and no other genomic/proteomic resources have been taken into account.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020

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Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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Section: Related Work and Motivationmentioning

confidence: 99%

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020

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“…In Liu et al [18] and Xue et al [19], authors have developed feature selection algorithms using multi view data for video, text, and image data sets. On the problem of gene selection, an unsupervised graph-theoretic multi-view clustering approach was proposed bt Swarnkar et al [20]. Though their proposed algorithm is a multi-view approach, here, different 'views' are developed by considering different feature subspaces over a gene expression data set.…”

Section: Related Work and Motivationmentioning

confidence: 99%

A consensus multi-view multi-objective gene selection approach for improved sample classification

2020

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Background In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different ‘omics’ resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. Results In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. Conclusions The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

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“…Liu et al [13] modeled multiview data as a tensor and developed a new tensor-based framework for the integration of heterogeneous multiview data in the context of spectral clustering. More recently, new learning technologies [14][15][16][17][18][19] have been introduced for multi-view data analysis and processing. When seeking to achieve consistent and common conclusions, these learning models also take into account the differences among multiple views.…”

Section: Introductionmentioning

confidence: 99%

A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Zeng

Wang

Cui

et al. 2018

IEEE Trans. Fuzzy Syst.

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Clustering is increasingly important for multiview data analytics and current algorithms are either based on the collaborative learning of local partitions or directly derived global clustering from multi-kernel learning. In this paper, we innovate a clustering model that unifies the local partitions and global clustering in a collaborative learning framework. We firstly construct a common multi-kernel space (CMKS) from a set of basis kernels to better reflect clustering information of each individual view. Then, considering that joint local partitions would conform to the global clustering, we fuse the local partitions and global clustering guidance as a single objective function in accordance with fuzzy clustering form. The collaborative learning strategy enables the mutual and interactive clustering from local partitions and global clustering. The validation was performed over two synthetic and four public databases and the clustering accuracy was measured by NMI and RI. The experimental results demonstrated that the proposed algorithm outperformed the related state-of-the-art algorithms in comparison which included multitask, multi-kernel and multiview clustering approaches.

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Graph-based unsupervised feature selection and multiview clustering for microarray data

Cited by 12 publications

References 39 publications

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

A consensus multi-view multi-objective gene selection approach for improved sample classification

A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

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