Multidimensional Scaling for Genomic Data

  • Audrone JakaitieneEmail author
  • Mara Sangiovanni
  • Mario R. Guarracino
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 107)


Scientists working with genomic data face challenges to analyze and understand an ever-increasing amount of data. Multidimensional scaling (MDS) refers to the representation of high dimensional data in a low dimensional space that preserves the similarities between data points. Metric MDS algorithms aim to embed inter-point distances as close as the input dissimilarities. The computational complexity of most metric MDS methods is over O(n2), which restricts application to large genomic data (n ≫ 106). The application of non-metric MDS might be considered, in which inter-point distances are embedded considering only the relative order of the input dissimilarities. A non-metric MDS method has lower complexity compared to a metric MDS, although it does not preserve the true relationships. However, if the input dissimilarities are unreliable, too difficult to measure or simply unavailable, a non-metric MDS is the appropriate algorithm. In this paper, we give overview of both metric and non-metric MDS methods and their application to genomic data analyses.


metric multidimensional scaling; non-metric multidimensional scaling; data mining; genomic data 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Audrone Jakaitiene
    • 1
    Email author
  • Mara Sangiovanni
    • 2
  • Mario R. Guarracino
    • 2
  • Panos M. Pardalos
    • 3
  1. 1.System Analysis Department, Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania
  2. 2.High Performance Computing and Networking InstituteNational Research CouncilNaplesItaly
  3. 3.Center for Applied OptimizationUniversity of FloridaGainesvilleUSA

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