Comparison of spectral similarity measures for compound identification

Imhoi Koo, Xiang Zhang, Seongho Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Linear and nonlinear error models were considered for compound identification using Gaussian noise. As for linear error models, additive Gaussian models were constructed and multiplicative Gaussian models were used for nonlinear error models. For each error model, three similarity measures, cosine correlation, Pearson's correlation, and Spearman's rank correlation, were implemented to compare their performance of compound identification. Furthermore, the effect of zero intensities was investigated by calculating the correlation using two schemes, OR-zero and ALL-zero methods. The simulation studies showed that the rank-based correlation, Spearman's correlation, was more robust than other correlations to all the noises and ALL-zero method provides more information than OR-zero for compound identification.

Original languageEnglish (US)
Title of host publication5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
DOIs
StatePublished - Jul 14 2011
Event5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011 - Wuhan, China
Duration: May 10 2011May 12 2011

Publication series

Name5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011

Conference

Conference5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
CountryChina
CityWuhan
Period5/10/115/12/11

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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