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

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

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.

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

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.

An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model

Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.

Analysis of the nucleotide content of Escherichia coli promoter sequences related to the alternative sigma factors

Promoters are DNA sequences located upstream of the transcription start site of genes. In bacteria, the RNA polymerase enzyme requires additional subunits, called sigma factors (σ) to begin specific gene transcription in distinct environmental conditions. Currently, promoter prediction still poses many challenges due to the characteristics of these sequences. In this paper, the nucleotide content of Escherichia coli promoter sequences, related to five alternative σ factors, was analyzed by a machine learning technique in order to provide profiles according to the σ factor which recognizes them. For this, the clustering technique was applied since it is a viable method for finding hidden patterns on a data set. As a result, 20 groups of sequences were formed, and, aided by the Weblogo tool, it was possible to determine sequence profiles. These found patterns should be considered for implementing computational prediction tools. In addition, evidence was found of an overlap between the functions of the genes regulated by different σ factors, suggesting that DNA structural properties are also essential parameters for further studies.