A consensus multi-view multi-objective gene selection approach for improved sample classification
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METHODOLOGY
Open Access
A consensus multi-view multi-objective gene selection approach for improved sample classification Sudipta Acharya1 , Laizhong Cui1* and Yi Pan2 From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020 *Correspondence: [email protected] 1 Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China Full list of author information is available at the end of the article
Abstract 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. (Continued on next page)
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