Prediction of normalized signal strength on DNA sequencing microarrays by n-grams within a neural network model.

We have shown previously that a feed-forward, back propagation neural network model based on composite n-grams can predict normalized signal strengths of a microarray based DNA sequencing experiment. The microarray comprises a 4xN set of 25-base single-stranded DNA molecule ('oligos'), specific for each of the four possible bases (A, C, G, or T) for Adenine, Cytosine, Guanine and Thymine respectively at each of N positions in the experimental DNA. Strength of binding between reference oligos and experimental DNA varies according to base complementarity and the strongest signal in any quartet should `call the base` at that position.