By Kenneth H. Buetow (auth.), Sarah Cohen-Boulakia, Val Tannen (eds.)
Understanding the mechanisms concerned with lifestyles (e. g. , gaining knowledge of the organic functionofasetofproteins,inferringtheevolutionofasetofspecies)isbecoming increasinglydependent onprogressmade inmathematics,computer science,and molecular engineering. For the prior 30 years, new high-throughput applied sciences were constructed producing quite a lot of info, dispensed throughout many information assets on the net, with a excessive measure of semantic heterogeneity and di?erentlevelsofquality. However,onesuchdatasetisnot,byitself,su?cientfor scienti?c discovery. as a substitute, it needs to be mixed with different information and processed via bioinformatics instruments for styles, similarities, and weird occurrences to be saw. either information integration and knowledge mining are hence of paramount significance in existence technological know-how. DILS 2007 was once the fourth in a workshop sequence that goals at fostering d- cussion, trade, and innovation in examine and improvement within the parts of information integration and knowledge administration for the existence sciences. each one past DILS workshop attracted round a hundred researchers from world wide. This 12 months, the variety of submitted papers back elevated. this system Committee - lected 19 papers out of fifty two complete submissions. The DILS 2007 papers conceal a large spectrum of theoretical and useful concerns together with scienti?c work?ows, - notation in information integration, mapping and matching ideas, and modeling of lifestyles technology info. one of the papers, we distinct thirteen papers offering examine on new types, equipment, or algorithms and six papers featuring imp- mentation of platforms or event with platforms in perform. as well as the offered papers, DILS 2007 featured keynote talks by means of Kenneth H. Buetow, nationwide melanoma Institute, and Junhyong Kim, collage of Pennsylvania.
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Extra info for Data Integration in the Life Sciences: 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007. Proceedings
Section 3 describes RmotifDB as well as its interface and search mechanisms. Section 4 presents techniques used to integrate RmotifDB with the Gene Ontology. Section 5 concludes the paper and points out some directions for future research. 2 A Motif Mining Method We have developed several structural motif mining methods based on diﬀerent RNA representation models. For example, in , we represented an RNA secondary structure using an ordered labeled tree and designed a tree matching algorithm to ﬁnd motifs in multiple RNA secondary structures.
5 Conclusion In this paper we presented an RNA structural motif database, called RmotifDB, and described some features of RmotifDB as well as techniques used for Toward an Integrated RNA Motif Database 35 integrating RmotifDB with the Gene Ontology. Besides integrating with the Gene Ontology, RmotifDB also supports standard formats such as accession numbers from ReqSeq as well as commonly used Gene IDs. Therefore, anyone familiar with data associated with these identiﬁcation numbers can retrieve the sequences without diﬃculty.
2. A data provider must be implemented. The instrument-specific data provider component tells B-Fabric from which location the experimental data has to be imported into B-Fabric. 3. A data mart has to be determined where the experimental data generated with this instrument shall be managed and analyzed in detail. In case there is no mart for that instrument, the B-Fabric custom mart has to be extended appropriately. 4. One or more instrument-specific workflows have to be specified using the XML notation of OSWorkflow.
Data Integration in the Life Sciences: 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007. Proceedings by Kenneth H. Buetow (auth.), Sarah Cohen-Boulakia, Val Tannen (eds.)