02937nas a2200205   4500008004100000022001400041245009900055210006900154300000900223490000700232520232500239653001902564653001702583653001402600653001202614100001902626700001902645700002002664856004702684       2009                        eng d  a1471-210500aSocial tagging in the life sciences: characterizing a new metadata resource for bioinformatics0 aSocial tagging in the life sciences characterizing a new metadat  a313+0 v103 aBACKGROUND:Academic social tagging systems, such as Connotea and CiteULike, provide researchers with a means to organize personal collections of online references with keywords (tags) and to share these collections with others. One of the side-effects of the operation of these systems is the generation of large, publicly accessible metadata repositories describing the resources in the collections. In light of the well-known expansion of information in the life sciences and the need for metadata to enhance its value, these repositories present a potentially valuable new resource for application developers. Here we characterize the current contents of two scientifically relevant metadata repositories created through social tagging. This investigation helps to establish how such socially constructed metadata might be used as it stands currently and to suggest ways that new social tagging systems might be designed that would yield better aggregate products.RESULTS:We assessed the metadata that users of CiteULike and Connotea associated with citations in PubMed with the following metrics: coverage of the document space, density of metadata (tags) per document, rates of inter-annotator agreement, and rates of agreement with MeSH indexing. CiteULike and Connotea were very similar on all of the measurements. In comparison to PubMed, document coverage and per-document metadata density were much lower for the social tagging systems. Inter-annotator agreement within the social tagging systems and the agreement between the aggregated social tagging metadata and MeSH indexing was low though the latter could be increased through voting.CONCLUSION:The most promising uses of metadata from current academic social tagging repositories will be those that find ways to utilize the novel relationships between users, tags, and documents exposed through these systems. For more traditional kinds of indexing-based applications (such as keyword-based search) to benefit substantially from socially generated metadata in the life sciences, more documents need to be tagged and more tags are needed for each document. These issues may be addressed both by finding ways to attract more users to current systems and by creating new user interfaces that encourage more collectively useful individual tagging behaviour.10abioinformatics10aconservation10adatabases10atagging1 aGood, Benjamin1 aTennis, Joseph1 aWilkinson, Mark  uhttp://dx.doi.org/10.1186/1471-2105-10-31301250nas a2200217   4500008004100000022001300041245010100054210006900155260001300224300001600237490000700253520061200260653000800872653001700880653001900897653001300916653001400929653001500943653002300958856005100981       2004                        eng d  a0031320300aClassification of bioacoustic time series based on the combination of global and local decisions0 aClassification of bioacoustic time series based on the combinati  cDecember  a2293–23050 v373 aAutomated classification of cricket songs from Thailand and Ecuador is the topic of this study. For this, the locations of pulses are determined and different features in the time and the frequency domain are extracted automatically from the time series. For the categorization of the sound patterns these features are combined through data fusion, temporal fusion and decision fusion. Local features and global features are distinguished. For the classification a fuzzy- k -nearest-neighbour classifier was used. Classification results for a data set containing songs of 28 different species are presented.10aasr10abioacoustics10aclassification10acrickets10adatabases10aorthoptera10asound\_recognition  uhttp://dx.doi.org/10.1016/j.patcog.2004.04.00401402nas a2200217   4500008004100000022001400041245008900055210006900144260000900213300001400222490000700236520077500243653001901018653001401037653001501051653001601066100002001082700001901102700002201121856004101143       2009                        eng d  a1477-405400aBiological knowledge management: the emerging role of the Semantic Web technologies.0 aBiological knowledge management the emerging role of the Semanti  cJuly  a392–4070 v103 aNew knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.10abioinformatics10adatabases10aontologies10asemanticweb1 aAntezana, Erick1 aKuiper, Martin1 aMironov, Vladimir  uhttp://dx.doi.org/10.1093/bib/bbp02402502nas a2200277   4500008004100000022001400041245004400055210004300099300000800142490000700150520176900157653002901926653001901955653001401974653001001988653002301998653001502021100002002036700001702056700002202073700002102095700001802116700002502134700001902159856004602178       2009                        eng d  a1471-216400aBioMart–biological queries made easy.0 aBioMart–biological queries made easy  a22+0 v103 aBACKGROUND: Biologists need to perform complex queries, often across a variety of databases. Typically, each data resource provides an advanced query interface, each of which must be learnt by the biologist before they can begin to query them. Frequently, more than one data source is required and for high-throughput analysis, cutting and pasting results between websites is certainly very time consuming. Therefore, many groups rely on local bioinformatics support to process queries by accessing the resource's programmatic interfaces if they exist. This is not an efficient solution in terms of cost and time. Instead, it would be better if the biologist only had to learn one generic interface. BioMart provides such a solution. RESULTS: BioMart enables scientists to perform advanced querying of biological data sources through a single web interface. The power of the system comes from integrated querying of data sources regardless of their geographical locations. Once these queries have been defined, they may be automated with its "scripting at the click of a button" functionality. BioMart's capabilities are extended by integration with several widely used software packages such as BioConductor, DAS, Galaxy, Cytoscape, Taverna. In this paper, we describe all aspects of BioMart from a user's perspective and demonstrate how it can be used to solve real biological use cases such as SNP selection for candidate gene screening or annotation of microarray results. CONCLUSION: BioMart is an easy to use, generic and scalable system and therefore, has become an integral part of large data resources including Ensembl, UniProt, HapMap, Wormbase, Gramene, Dictybase, PRIDE, MSD and Reactome. BioMart is freely accessible to use at http://www.biomart.org.10abiodiversity-informatics10abioinformatics10adatabases10aquery10asemanticenrichment10asematicweb1 aSmedley, Damian1 aHaider, Syed1 aBallester, Benoit1 aHolland, Richard1 aLondon, Darin1 aThorisson, Gudmundur1 aKasprzyk, Arek  uhttp://dx.doi.org/10.1186/1471-2164-10-22