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Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis.
Copyright policy )Abstract Motivation Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse. Results We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series’ metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study’s description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.
Microsoft Academic Graph classification: Restructuring Genome dynamics Reuse Information retrieval Gene expression omnibus Geo database Usability business.industry business Big data Metadata Computer science
Data Mining, Database Management Systems, Databases, Genetic, Gene Expression, Metadata, Metagenomics, Database Tool, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology, Information Systems
Data Mining, Database Management Systems, Databases, Genetic, Gene Expression, Metadata, Metagenomics, Database Tool, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology, Information Systems
Microsoft Academic Graph classification: Restructuring Genome dynamics Reuse Information retrieval Gene expression omnibus Geo database Usability business.industry business Big data Metadata Computer science
- The University of Texas Health Science Center at Houston United States
- Universidad Antonio Nariño Colombia
- University of Rochester United States
- UNIVERSITY OF ROCHESTER
- UNIVERSITY OF ROCHESTER United States
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- The University of Texas Health Science Center at Houston United States
- Universidad Antonio Nariño Colombia
- University of Rochester United States
- UNIVERSITY OF ROCHESTER
- UNIVERSITY OF ROCHESTER United States
- Universidad Antonio Nariño Colombia
- School of Biomedical Informatics (SBMI), University of Texas Health Science Center (UTHealth), Houston, TX, USA United States
Abstract Motivation Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse. Results We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series’ metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study’s description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.