Abstract
BackgroundSingular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related. SVD was initially developed to reduce the time needed for information retrieval and analysis of very large data sets in the complex internet environment. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could also facilitate data analysis in this research area.ResultsWe found that SVD applied to amino acid sequences demonstrates relationships and provides a basis for producing clusters and cladograms, demonstrating evolutionary relatedness of species that correlates well with Linnaean taxonomy. The choice of a reasonable number of singular values is crucial for SVD-based studies. We found that fewer singular values are needed to produce biologically significant clusters when SVD is employed. Subsequently, we developed a method to determine the lowest number of singular values and fewest clusters needed to guarantee biological significance; this system was developed and validated by comparison with Linnaean taxonomic classification.ConclusionsBy using SVD, we can reduce uncertainty concerning the appropriate rank value necessary to perform accurate information retrieval analyses. In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.
Highlights
Singular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related
The choice was made by K-Means [11], Expectation Maximization (EM) [12], Adaptive Quality-based Clustering Algorithm (AQBC) [13], K-Medoids [14], and MakeDensityBasedClusterer (MDBC) [15], since there is a statistically well-founded background, they have been widely used, and they are available as free software packages from R [16], Waikato Environment for Knowledge Analysis (WEKA) [15], and the JAVA Machine Learning Library [17]
The K-Means requires that an array of numbers be processed to calculate distances for the creation of clusters. It opens the possibility of including a parameter that defines a fixed number of clusters to be created with the elements in the distance matrix
Summary
Singular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could facilitate data analysis in this research area. We developed a methodology, based on singular value decomposition (SVD), for improved inference of evolutionary relationships between amino acid sequences of different species [1]. SVD produces a revised distance matrix for a set of related elements. The reason we chose this methodology is the proven capacity that SVD has to establish non-obvious, relevant relationships among clustered elements [2][3][4][5], providing a deterministic method for grouping related species. A matrix with a singular value decomposition of matrix A can be made:
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