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Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:p> 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 \u201csplicing-filling method\u201d and the \u201cmatching-filling method\u201d 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. <\/jats:p>","DOI":"10.1142\/s0219720024500276","type":"journal-article","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T05:31:21Z","timestamp":1733549481000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on similarity retrieval method based on mass spectral entropy"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9857-7807","authenticated-orcid":false,"given":"Li-Ping","family":"Wu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. 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