MChip: a tool for influenza surveillance.
The design and characterization of a low-density microarray for subtyping influenza A is presented. The microarray consisted of 15 distinct oligonucleotides designed to target only the matrix gene segment of influenza A. An artificial neural network was utilized to automate microarray image interpretation. The neural network was trained to recognize fluorescence image patterns for 68 known influenza viruses and subsequently used to identify 53 unknowns in a blind study that included 39 human patient samples and 14 negative control samples. The assay exhibited a clinical sensitivity of 95% and clinical specificity of 92%.
Dawson
Erica D
ED
Department of Chemistry and Biochemistry, UCB 215, University of Colorado at Boulder, Boulder, Colorado 80309, USA.
Moore
Chad L
CL
Smagala
James A
JA
Dankbar
Daniela M
DM
Mehlmann
Martin
M
Townsend
Michael B
MB
Smith
Catherine B
CB
Cox
Nancy J
NJ
Kuchta
Robert D
RD
Rowlen
Kathy L
KL
D001331:Automation; D004591:Electrophoresis, Polyacrylamide Gel; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D009980:Influenza A virus / Q000145:classification* / Q000235:genetics; D008856:Microscopy, Fluorescence; D025202:Molecular Diagnostic Techniques; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009975:Orthomyxoviridae / Q000145:classification / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity