{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:10:33Z","timestamp":1772172633945,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1007450","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:00:00Z","timestamp":1605657600000}}],"reference-count":62,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["R01GM126558"],"award-info":[{"award-number":["R01GM126558"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and\/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1007450","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T13:35:47Z","timestamp":1604669747000},"page":"e1007450","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Maximizing the reusability of gene expression data by predicting missing metadata"],"prefix":"10.1371","volume":"16","author":[{"given":"Pei-Yau","family":"Lung","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3301-325X","authenticated-orcid":true,"given":"Dongrui","family":"Zhong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5985-0470","authenticated-orcid":true,"given":"Xiaodong","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7429-7615","authenticated-orcid":true,"given":"Jinfeng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"pcbi.1007450.ref001","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets\u2014update","volume":"41","author":"T Barrett","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"7675","key":"pcbi.1007450.ref002","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nature24277","article-title":"Genetic effects on gene expression across human tissues.","volume":"550","author":"GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)\u2014Analysis Working Group, Statistical Methods groups\u2014Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH\/NCI","year":"2017","journal-title":"Nature"},{"issue":"7146","key":"pcbi.1007450.ref003","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1038\/nature05874","article-title":"Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project","volume":"447","author":"ENCODE Project Consortium","year":"2007","journal-title":"Nature"},{"issue":"5","key":"pcbi.1007450.ref004","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.gpb.2014.10.001","article-title":"Big biological data: challenges and opportunities","volume":"12","author":"Y. 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