ArticleBMC bioinformatics2006
MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 44 citations in OpenAlex.
- Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.Current drug delivery · 2024Review
- Cognitive analysis of metabolomics data for systems biology.Nature protocols · 2021Article
- Semantic Analysis of Posttranslational Modification of Proteins Accumulated in Thyroid Cancer Cells Exposed to Simulated Microgravity.International journal of molecular sciences · 2018Article
- A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.FEBS letters · 2013Article
- Bioinformatics tools for cancer metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2011Article
- Data standards for Omics data: the basis of data sharing and reuse.Methods in molecular biology (Clifton, N.J.) · 2011Review
- Systematic integration of experimental data and models in systems biology.BMC bioinformatics · 2010Article
- Database resources in metabolomics: an overview.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2010Review
- Facilitating the development of controlled vocabularies for metabolomics technologies with text mining.BMC bioinformatics · 2008Article
- Highly-parallel metabolomics approaches using LC-MS for pharmaceutical and environmental analysis.Trends in analytical chemistry : TRAC · 2007Article
- Growth control of the eukaryote cell: a systems biology study in yeast.Journal of biology · 2007Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/.
conclusionThe maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.