A Grid-based HIV expert system.
OBJECTIVE
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.
Sloot
Peter M A
PM
Section Computational Science, University of Amsterdam, Kruislaan 403, 1098, Amsterdam, SJ, The Netherlands. sloot@science.uva.nl
Boukhanovsky
Alexander V
AV
Keulen
Wilco
W
Tirado-Ramos
Alfredo
A
Boucher
Charles A
CA
D001699:Biometry; D019295:Computational Biology; D003195:Computer Communication Networks; D016208:Databases, Factual; D005103:Expert Systems; D015658:HIV Infections; D015497:HIV-1; D006801:Humans; D018511:Systems Integration