DMCA
An Evaluation of Text Retrieval Methods for Similarity Search of multi-dimensional NMR-Spectra
BibTeX
@MISC{Hinneburg_anevaluation,
author = {Er Hinneburg and Andrea Porzel and Karina Wolfram},
title = {An Evaluation of Text Retrieval Methods for Similarity Search of multi-dimensional NMR-Spectra},
year = {}
}
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Abstract
Abstract. Searching and mining nuclear magnetic resonance (NMR)spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete textlike data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful ”topics” in the NMR-data. 1
Keyphrases
text retrieval method similarity search nuclear magnetic resonance textlike data important task probabilistic latent semantic indexing relational object continuous nmr-spectra mapped nmr-data originating text data hidden topic standard vector space model simple direct similarity function 2d-nmr data interesting natural product several mapping useful chemical compound continuous multi-dimensional point meaningful topic vector space model retrieval method
