LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Back to previous search Search history
Advanced search

Your last searches

  1. AU=ElGokhy Sherin M
  2. AU="Stegmaier, Sabine"
  3. AU="Simons, Gemma N"
  4. AU="Domínguez-Zorita, Sonia"
  5. AU="Nakashima, Ayaka"
  6. AU="Skorecki, Karl"
  7. AU=Ibrahim Salwa
  8. AU=Geocadin Romergryko G
  9. AU="Leroy, J"
  10. AU="Wilson, Peter H"
  11. AU="Cunha, Carla Baroni"

Search results

Result 1 - 2 of total 2

Search options

  1. Article ; Online: ROHDIP

    Wael Shehab / Sherin M. ElGokhy / ElSayed Sallam

    International Journal of Advanced Computer Science and Applications, Vol 7, Iss 9, Pp 104-

    Resource Oriented Heterogeneous Data Integration Platform

    2016  Volume 109

    Abstract: During the last few years, the revolution of social networks such as Facebook, Twitter, and Instagram led to a daily increasing of data that are heterogeneous in their sources, data models, and platforms. Heterogeneous data sources have many forms such ... ...

    Abstract During the last few years, the revolution of social networks such as Facebook, Twitter, and Instagram led to a daily increasing of data that are heterogeneous in their sources, data models, and platforms. Heterogeneous data sources have many forms such as the www, deep web, relational databases systems, No-SQL database systems, hierarchal data systems, semi-structured files, in which data are usually allocated on different machines (distributed) and have different data models (heterogeneous). Large-scale data integration efforts demonstrate that their most valuable contribution is implementing a data integration platform that provides a uniform access to the heterogeneous data sources, as well as the different versions of data reported by the same data source over time. Furthermore, the platform must be able to integrate data from a broad range of data authoring devices and database management systems. It also should be accessible by almost types of data querying devices to ensure globally querying the integration platform from any place on earth anytime and receiving the query result in any data format. In this paper, we create a resource oriented heterogeneous data integration platform (ROHDIP) that facilitates the data integration process and implements the objectives discussed above. We use the resource oriented architecture ROA to support the uniform access by most types of data querying devices from anywhere and to improve the query response time.
    Keywords Data Integration ; Data heterogeneity ; SOA ; ROA ; Restful ; ROHDIP ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Subject code 004
    Language English
    Publishing date 2016-09-01T00:00:00Z
    Publisher The Science and Information (SAI) Organization
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Ensemble-based classification approach for micro-RNA mining applied on diverse metagenomic sequences.

    ElGokhy, Sherin M / ElHefnawi, Mahmoud / Shoukry, Amin

    BMC research notes

    2014  Volume 7, Page(s) 286

    Abstract: Background: MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their ... ...

    Abstract Background: MicroRNAs (miRNAs) are endogenous ∼22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their low expression, low stability, tissue specificity and the high cost of the cloning procedure. Thus, computational identification of miRNAs from genomic sequences provide a valuable complement to cloning. Different approaches for identification of miRNAs have been proposed based on homology, thermodynamic parameters, and cross-species comparisons.
    Results: The present paper focuses on the integration of miRNA classifiers in a meta-classifier and the identification of miRNAs from metagenomic sequences collected from different environments. An ensemble of classifiers is proposed for miRNA hairpin prediction based on four well-known classifiers (Triplet SVM, Mipred, Virgo and EumiR), with non-identical features, and which have been trained on different data. Their decisions are combined using a single hidden layer neural network to increase the accuracy of the predictions. Our ensemble classifier achieved 89.3% accuracy, 82.2% f-measure, 74% sensitivity, 97% specificity, 92.5% precision and 88.2% negative predictive value when tested on real miRNA and pseudo sequence data. The area under the receiver operating characteristic curve of our classifier is 0.9 which represents a high performance index.The proposed classifier yields a significant performance improvement relative to Triplet-SVM, Virgo and EumiR and a minor refinement over MiPred.The developed ensemble classifier is used for miRNA prediction in mine drainage, groundwater and marine metagenomic sequences downloaded from the NCBI sequence reed archive. By consulting the miRBase repository, 179 miRNAs have been identified as highly probable miRNAs. Our new approach could thus be used for mining metagenomic sequences and finding new and homologous miRNAs.
    Conclusions: The paper investigates a computational tool for miRNA prediction in genomic or metagenomic data. It has been applied on three metagenomic samples from different environments (mine drainage, groundwater and marine metagenomic sequences). The prediction results provide a set of extremely potential miRNA hairpins for cloning prediction methods. Among the ensemble prediction obtained results there are pre-miRNA candidates that have been validated using miRbase while they have not been recognized by some of the base classifiers.
    MeSH term(s) Metagenome ; MicroRNAs/genetics ; Support Vector Machine
    Chemical Substances MicroRNAs
    Language English
    Publishing date 2014-05-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2413336-X
    ISSN 1756-0500 ; 1756-0500
    ISSN (online) 1756-0500
    ISSN 1756-0500
    DOI 10.1186/1756-0500-7-286
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top