The Zebrafish Information Network (ZFIN): a resource for genetic, genomic and developmental research

The Zebrafish Information Network, ZFIN, is a WWW community resource of zebrafish genetic, genomic and developmental research information (http://zfin.org). ZFIN provides an anatomical atlas and dictionary, developmental staging criteria, research methods, pathology information and a link to the ZFIN relational database (http://zfin.org/ZFIN/). The database, built on a relational, object-oriented model, provides integrated information about mutants, genes, genetic markers, mapping panels, publications and contact information for the zebrafish research community. The database is populated with curated published data, user submitted data and large dataset uploads. A broad range of data types including text, images, graphical representations and genetic maps supports the data. ZFIN incorporates links to other genomic resources that provide sequence and ortholog data. Zebrafish nomenclature guidelines and an automated registration mechanism for new names are provided. Extensive usability testing has resulted in an easy to learn and use forms interface with complex searching capabilities.

BIND—The Biomolecular Interaction Network Database

The Biomolecular Interaction Network Database (BIND; http://binddb.org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.