Predicting Biomedical Metadata in CEDAR: a study of gene expression metadata in GEO

TitlePredicting Biomedical Metadata in CEDAR: a study of gene expression metadata in GEO
Publication TypeJournal Article
Year of Publication2017
AuthorsPanahiazar M, Dumontier M, Gevaert O
JournalJournal of Biomedical Informatics
Date PublishedJune, 2017
Type of ArticleManuscript

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata predic-tion framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of ex-perimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata ele-ments (sample type, molecular type, platform, label type and organism) from over 1.3 million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average perfor-mance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining al-gorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse.

Last Updated: 
Jul 11 2017 - 1:08pm