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Research on similarity retrieval method based on mass spectral entropy.

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Title: Research on similarity retrieval method based on mass spectral entropy.
Authors: Wu, Li-Ping1 (AUTHOR) 1575250952@qq.com, Yong, Li1 (AUTHOR) 12309058@kust.edu.cn, Cheng, Xiang2 (AUTHOR) xcheng0871@163.com, Zhou, Yang3 (AUTHOR) yangzhou@nature-standard.com
Source: Journal of Bioinformatics & Computational Biology. Dec2024, Vol. 22 Issue 6, p1-18. 18p.
Subjects: Tandem mass spectrometry, Mass spectrometry, Entropy (Information theory), Information measurement, Small molecules
Abstract: Compound identification in small molecule research relies on comparing experimental mass spectra with mass spectral databases. However, unequal data lengths often lead to inefficient and inaccurate retrieval. Moreover, the similarity calculation methods used by commercial software have limitations. To address these issues, two mass spectrometry data processing methods namely the "splicing-filling method" and the "matching-filling method" have been proposed. In addition, an information entropy-based similarity calculation method for mass spectra is presented. The alignment method converts mass spectra of different lengths for unknown and known compounds into equal-length mass spectra, allowing more accurate calculation of similarities between mass spectra. Information entropy measurements are used to quantify the differences in intensity distributions in the aligned mass spectral data, which are then used to compare the degree of similarity between different mass spectra. The results of the example validation show that the two data alignment methods can effectively solve the problem of unequal lengths of mass spectral data in similarity calculation. The results of the mass spectral entropy method are reliable and suitable for the identification of mass spectra. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Bioinformatics & Computational Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 183079195
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Research on similarity retrieval method based on mass spectral entropy.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Li-Ping%22">Wu, Li-Ping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1575250952@qq.com</i><br /><searchLink fieldCode="AR" term="%22Yong%2C+Li%22">Yong, Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 12309058@kust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Xiang%22">Cheng, Xiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xcheng0871@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yang%22">Zhou, Yang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> yangzhou@nature-standard.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Bioinformatics+%26+Computational+Biology%22">Journal of Bioinformatics & Computational Biology</searchLink>. Dec2024, Vol. 22 Issue 6, p1-18. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Tandem+mass+spectrometry%22">Tandem mass spectrometry</searchLink><br /><searchLink fieldCode="DE" term="%22Mass+spectrometry%22">Mass spectrometry</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy+%28Information+theory%29%22">Entropy (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+measurement%22">Information measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Small+molecules%22">Small molecules</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Compound identification in small molecule research relies on comparing experimental mass spectra with mass spectral databases. However, unequal data lengths often lead to inefficient and inaccurate retrieval. Moreover, the similarity calculation methods used by commercial software have limitations. To address these issues, two mass spectrometry data processing methods namely the "splicing-filling method" and the "matching-filling method" have been proposed. In addition, an information entropy-based similarity calculation method for mass spectra is presented. The alignment method converts mass spectra of different lengths for unknown and known compounds into equal-length mass spectra, allowing more accurate calculation of similarities between mass spectra. Information entropy measurements are used to quantify the differences in intensity distributions in the aligned mass spectral data, which are then used to compare the degree of similarity between different mass spectra. The results of the example validation show that the two data alignment methods can effectively solve the problem of unequal lengths of mass spectral data in similarity calculation. The results of the mass spectral entropy method are reliable and suitable for the identification of mass spectra. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Bioinformatics & Computational Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1142/S0219720024500276
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
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    Subjects:
      – SubjectFull: Tandem mass spectrometry
        Type: general
      – SubjectFull: Mass spectrometry
        Type: general
      – SubjectFull: Entropy (Information theory)
        Type: general
      – SubjectFull: Information measurement
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      – SubjectFull: Small molecules
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      – TitleFull: Research on similarity retrieval method based on mass spectral entropy.
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            NameFull: Wu, Li-Ping
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            NameFull: Yong, Li
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            NameFull: Cheng, Xiang
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            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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            – TitleFull: Journal of Bioinformatics & Computational Biology
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