Mobile interaction and query optimization in a protein-ligand data analysis system

Marvin Lapeine, Katherine G. Herbert, Emily Hill, Nina M. Goodey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With current trends in integrating phylogenetic analysis into pharma-research, Computing systems that integrate the two areas can help the drug discovery field. DrugTree is a tool that overlays ligand data on a protein-motivated phylogenetic tree. While initial tests of DrugTree are successful, it has been noticed that there are a number of lags concerning querying the tree. Due to the interleaving nature of the data, query optimization can become problematic since the data is being obtained from multiple sources, integrated and then presented to the user with the phylogenetic imposed upon the phylogenetic analysis layer. This poster presents our initial methodologies for addressing the query optimization issues. Our approach applies standards as well as uses novel mechanisms to help improve performance time.

Original languageEnglish
Title of host publicationSIGMOD 2013 - International Conference on Management of Data
Number of pages1
DOIs
StatePublished - 29 Jul 2013
Event2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013 - New York, NY, United States
Duration: 22 Jun 201327 Jun 2013

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013
CountryUnited States
CityNew York, NY
Period22/06/1327/06/13

Keywords

  • Bioinformatics
  • Data integration
  • Pharmaceutical informatics
  • Phylogenetics
  • Query processing

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  • Cite this

    Lapeine, M., Herbert, K. G., Hill, E., & Goodey, N. M. (2013). Mobile interaction and query optimization in a protein-ligand data analysis system. In SIGMOD 2013 - International Conference on Management of Data (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/2463676.2465344