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The expression data used included the simulated microarray noise that GeneNetWeaver is capable of producing.
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We performed simulation studies based on known haplotypes and real microarray noise, demonstrating that this tool successfully inferred haplotypes and diplotypes from noisy microarray data, and that the tool even had an ability to correct total copy numbers and unphased genotypes that are wrongly determined due to noise.
Gene expression microarray experiments are often affected by noise that is caused by the experimental design of the underlying microarray technique, the stages of sample preparation, and the hybridisation processes of oligonucleotide probes [ 7].
From the observation, we filters almost 75% genes from each microarray dataset that means microarray technology can measure thousands of genes simultaneously, but it also contains much noise that causes a lot of missing values.
Is there noise that could be reduced?
We acknowledge the potential bias and noise that microarray data may bear.
Emphasis is placed on describing approaches and techniques that help to minimize the artifacts and noise that so often plague microarray data.
Secondly, the limited number of samples that a microarray is typically conducted upon, may introduce significant "measurement noise" that compromises the accuracy of the underlying correlations.
We used simulated datasets that were as close to real haplotypes and microarray noise levels as possible, and we demonstrated successful estimation by our algorithm.
Add some noise -- white noise that is.
However, signal values were well above the microarray noise.
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