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Biomedical discovery acceleration, with applications to craniofacial development.

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Pubmed ID: 19325874 RIS Download

Mesh terms: Animals | Artificial Intelligence | Database Management Systems | Databases, Protein | Facial Bones | Information Storage and Retrieval | Mice | Natural Language Processing | Periodicals as Topic | Proteome

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HGNC

A worldwide authority that approves standardized nomenclature to gene name and symbol (short-form abbreviation) for each known human gene and stores all approved symbols in the HGNC database. Approved symbols are also browse-able by chromosome. Each symbol is unique and each gene is only given one approved gene symbol. In preference each symbol maintains parallel construction in different members of a gene family and can also be used in other species, especially the mouse. Over 38,000 symbols have been approved; the vast majority of these are for protein-coding genes, but also include symbols for pseudogenes, non-coding RNAs, phenotypes and genomic features. Individual new symbols are requested by scientists, journals and databases, and groups of new symbols by those working on gene families or specific regions of the genome. Gene symbol and name proposals may be submitted to them to be accredited with HGNC approved nomenclature for use in publications, databases and presentations.

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IntAct

Open source database system and analysis tools for molecular interaction data. All interactions are derived from literature curation or direct user submissions. Direct user submissions of molecular interaction data are encouraged, which may be deposited prior to publication in a peer-reviewed journal. The IntAct Database contains (Jun. 2014): * 447368 Interactions * 33021 experiments * 12698 publications * 82745 Interactors IntAct provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows "zooming in" on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available.

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Gene Expression Omnibus

A public functional genomics data repository supporting MIAME-compliant data submissions. Tools are provided to help users query and download experiments and curated gene expression profiles. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted.

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