<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Jeong J</submitter><funding>NIGMS NIH HHS</funding><pagination>123</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3637833</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>14</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.&lt;h4>Results&lt;/h4>The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Comp</pubmed_abstract><journal>BMC bioinformatics</journal><pubmed_title>An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS.</pubmed_title><pmcid>PMC3637833</pmcid><funding_grant_id>1R01GM087735</funding_grant_id><pubmed_authors>Kim S</pubmed_authors><pubmed_authors>Jeong J</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Shen C</pubmed_authors><pubmed_authors>Shi X</pubmed_authors></additional><is_claimable>false</is_claimable><name>An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS.</name><description>&lt;h4>Background&lt;/h4>Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.&lt;h4>Results&lt;/h4>The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Comp</description><dates><release>2013-01-01T00:00:00Z</release><publication>2013 Apr</publication><modification>2025-04-21T23:44:56.814Z</modification><creation>2019-03-27T01:08:00Z</creation></dates><accession>S-EPMC3637833</accession><cross_references><pubmed>23575005</pubmed><doi>10.1186/1471-2105-14-123</doi></cross_references></HashMap>