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Similarity Search for Multi-dimensional NMR-Spectra of Natural Products

  • Karina Wolfram
  • Andrea Porzel
  • Alexander Hinneburg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.

Keywords

Grid Cell Similarity Search Latent Dirichlet Allocation Text Retrieval Grid Cell Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Karina Wolfram
    • 1
  • Andrea Porzel
    • 2
  • Alexander Hinneburg
    • 1
  1. 1.Institute of Computer ScienceMartin-Luther-University of Halle-WittenbergGermany
  2. 2.Leibniz Institute of Plant Biochemistry (IPB)Germany

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