{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:01:54Z","timestamp":1769850114566,"version":"3.49.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,12,20]],"date-time":"2017-12-20T00:00:00Z","timestamp":1513728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH105561, R01GM124061"],"award-info":[{"award-number":["R01MH105561, R01GM124061"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations between feature intensities to build a feature-level network. The algorithm uses support vector regression for missing value imputation based on features in the neighborhood on the network. We compared our proposed method with methods being widely used. As judged by the normalized root mean squared error in real data-based simulations, our proposed methods can achieve better accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package is available at http:\/\/web1.sph.emory.edu\/users\/tyu8\/MINMA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx816","type":"journal-article","created":{"date-parts":[[2017,12,19]],"date-time":"2017-12-19T20:17:12Z","timestamp":1513714632000},"page":"1555-1561","source":"Crossref","is-referenced-by-count":24,"title":["Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations"],"prefix":"10.1093","volume":"34","author":[{"given":"Zhuxuan","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5643-2668","authenticated-orcid":false,"given":"Jian","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA"}]},{"given":"Tianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,12,20]]},"reference":[{"key":"2023012713022337800_btx816-B1","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1093\/bioinformatics\/btq567","article-title":"Pathway activity profiling (papi): from the metabolite profile to the metabolic pathway activity","volume":"26","author":"Aggio","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012713022337800_btx816-B2","doi-asserted-by":"crossref","first-page":"3050","DOI":"10.1002\/elps.201500352","article-title":"Missing value imputation strategies for metabolomics data","volume":"36","author":"Armitage","year":"2015","journal-title":"Electrophoresis"},{"key":"2023012713022337800_btx816-B3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/1471-2105-13-99","article-title":"Metamapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity","volume":"13","author":"Barupal","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2023012713022337800_btx816-B4","doi-asserted-by":"crossref","first-page":"S298","DOI":"10.1111\/j.1532-5415.2010.03107.x","article-title":"Predictive health: the imminent revolution in health care","volume":"58","author":"Brigham","year":"2010","journal-title":"J. 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