View Item 
      •   Home
      • 1. Schools
      • College of Engineering
      • School of Computer Science and Engineering (SCSE)
      • SCSE Journal Articles
      • View Item
      •   Home
      • 1. Schools
      • College of Engineering
      • School of Computer Science and Engineering (SCSE)
      • SCSE Journal Articles
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.
      Subject Lookup

      Browse

      All of DR-NTUCommunities & CollectionsTitlesAuthorsBy DateSubjectsThis CollectionTitlesAuthorsBy DateSubjects

      My Account

      Login

      Statistics

      Most Popular ItemsStatistics by CountryMost Popular Authors

      About DR-NTU

      A grid-based HIV expert system

      Thumbnail
      Author
      Sloot, Peter M. A.
      Boukhanovsky, Alexander V.
      Keulen, Wilco.
      Tirado-Ramos, Alfredo.
      Boucher, Charles A. B.
      Date of Issue
      2005
      School
      School of Computer Engineering
      Abstract
      Objectives.This paper addresses Grid-based integration and access of distributed data from infectious disease patient databases, literature on in-vitro and in-vivo pharmaceutical data, mutation databases, clinical trials, simulations and medical expert knowledge. Methods. Multivariate analyses combined with rule-based fuzzy logic are applied to the integrated data to provide ranking of patient-specific drugs. In addition, cellular automata-based simulations are used to predict the drug behaviour over time. Access to and integration of data is done through existing Internet servers and emerging Grid-based frameworks like Globus. Data presentation is done by standalone PC based software, Web-access and PDA roaming WAP access. The experiments were carried out on the DAS2, a Dutch Grid testbed. Results. The output of the problem-solving environment (PSE) consists of a prediction of the drug sensitivity of the virus, generated by comparing the viral genotype to a relational database which contains a large number of phenotype-genotype pairs.Conclusions. Artificial Intelligence and Grid technology are effectively used to abstract knowledge from the data and provide the physicians with adaptive interactive advice on treatment applied to drug resistant HIV. An important aspect of our research is to use a variety of statistical and numerical methods to identify relationships between HIV genetic sequences and antiviral resistance to investigate consistency of results.
      Type
      Journal Article
      Series/Journal Title
      Journal of clinical monitoring and computing
      Rights
      © 2005 Springer.
      Collections
      • SCSE Journal Articles
      http://dx.doi.org/10.1007/s10877-005-0673-2
      Get published version (via Digital Object Identifier)

      Show full item record


      NTU Library, Nanyang Avenue, Singapore 639798 © 2011 Nanyang Technological University. All rights reserved.
      DSpace software copyright © 2002-2015  DuraSpace
      Contact Us | Send Feedback
      Share |    
      Theme by 
      Atmire NV
       

       


      NTU Library, Nanyang Avenue, Singapore 639798 © 2011 Nanyang Technological University. All rights reserved.
      DSpace software copyright © 2002-2015  DuraSpace
      Contact Us | Send Feedback
      Share |    
      Theme by 
      Atmire NV
       

       

      DCSIMG