<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Karam A</submitter><funding>57357 Hospital</funding><funding>Science, Technology &amp; Innovation Funding Authority (STDF)</funding><funding>Science, Technology &amp;amp; Innovation Funding Authority</funding><pagination>26</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12918191</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>18(1)</volume><pubmed_abstract>The expansion of untargeted metabolomics has made publicly accessible spectral libraries indispensable for metabolite annotation and machine learning applications. Enhancing the quality and consistency of these libraries is crucial for improving the accuracy of metabolite identification and training machine learning models. However, public spectral libraries often suffer from variability in user submissions, unintentional errors, and a lack of standardization. Existing metadata cleaning and normalization tools typically exclude spectra with incorrect or unsupported metadata rather than attempting to correct them, resulting in the loss of valuable spectral data and associated metabolites details. This study introduces STRIKER (SpecTRal lIbrary maKER), a repair tool specifically designed to </pubmed_abstract><journal>Journal of cheminformatics</journal><pubmed_title>STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries.</pubmed_title><pmcid>PMC12918191</pmcid><funding_grant_id>AI 42547</funding_grant_id><pubmed_authors>Ramzy A</pubmed_authors><pubmed_authors>Osama A</pubmed_authors><pubmed_authors>Karam A</pubmed_authors><pubmed_authors>Youssef NA</pubmed_authors><pubmed_authors>Mokhtar M</pubmed_authors><pubmed_authors>Abdelmoneim TK</pubmed_authors><pubmed_authors>Magdeldin S</pubmed_authors><pubmed_authors>Sabar N</pubmed_authors></additional><is_claimable>false</is_claimable><name>STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries.</name><description>The expansion of untargeted metabolomics has made publicly accessible spectral libraries indispensable for metabolite annotation and machine learning applications. Enhancing the quality and consistency of these libraries is crucial for improving the accuracy of metabolite identification and training machine learning models. However, public spectral libraries often suffer from variability in user submissions, unintentional errors, and a lack of standardization. Existing metadata cleaning and normalization tools typically exclude spectra with incorrect or unsupported metadata rather than attempting to correct them, resulting in the loss of valuable spectral data and associated metabolites details. This study introduces STRIKER (SpecTRal lIbrary maKER), a repair tool specifically designed to </description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Jan</publication><modification>2026-07-16T10:04:04.171Z</modification><creation>2026-07-09T10:48:36.009Z</creation></dates><accession>S-EPMC12918191</accession><cross_references><pubmed>41593726</pubmed><doi>10.1186/s13321-026-01150-4</doi></cross_references></HashMap>