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Formal Methods in Computing
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biocloud:ccpe:13 (Article)
Author(s) Concetto Spampinato, Isaak Kavasidis, Marco Aldinucci, Carmelo Pino, Daniela Giordano and Alberto Faro
Title« Discovering Biological Knowledge by Integrating High Throughput Data and Scientific Literature on the Cloud »
JournalConcurrency and Computation: Practice and Experience
Volume26
Number10
Page(s)1771-1786
Year2014
URLhttp://dx.doi.org/10.1002/cpe.3130
Abstract & Keywords
In this paper, we present a bioinformatics knowledge discovery tool for extracting and validating associations between biological entities. By mining specialised scientific literature, the tool not only generates biological hypotheses in the form of associations between genes, proteins, miRNA and diseases, but also validates the plausibility of such associations against high-throughput biological data (e.g. microarray) and annotated databases (e.g. Gene Ontology). Both the knowledge discovery system and its validation are carried out by exploiting the advantages and the potentialities of the Cloud, which allowed us to derive and check the validity of thousands of biological associations in a reasonable amount of time. The system was tested on a dataset containing more than 1000 gene-disease associations achieving an average recall of about 71%, outperforming existing approaches. The results also showed that porting a data-intensive application in an IaaS cloud environment boosts significantly the application's efficiency.

Keywords: cloud

BibTeX code

@article{biocloud:ccpe:13,
  volume = {26},
  number = {10},
  author = {Concetto Spampinato and Isaak Kavasidis and Marco Aldinucci and
            Carmelo Pino and Daniela Giordano and Alberto Faro},
  keywords = {cloud},
  url = {http://dx.doi.org/10.1002/cpe.3130},
  abstract = {In this paper, we present a bioinformatics knowledge discovery
              tool for extracting and validating associations between biological
              entities. By mining specialised scientific literature, the tool
              not only generates biological hypotheses in the form of
              associations between genes, proteins, miRNA and diseases, but also
              validates the plausibility of such associations against
              high-throughput biological data (e.g. microarray) and annotated
              databases (e.g. Gene Ontology). Both the knowledge discovery
              system and its validation are carried out by exploiting the
              advantages and the potentialities of the Cloud, which allowed us
              to derive and check the validity of thousands of biological
              associations in a reasonable amount of time. The system was tested
              on a dataset containing more than 1000 gene-disease associations
              achieving an average recall of about 71\%, outperforming existing
              approaches. The results also showed that porting a data-intensive
              application in an IaaS cloud environment boosts significantly the
              application's efficiency.},
  title = {Discovering Biological Knowledge by Integrating High Throughput Data
           and Scientific Literature on the Cloud},
  journal = {Concurrency and Computation: Practice and Experience},
  pages = {1771-1786},
  year = {2014},
}


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