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Bioinformation. 2019 May 30;15(6):388-393. doi: 10.6026/97320630015388. eCollection 2019.

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

Author information

1
1Program in Scientific Computing, Memorial University of Newfoundland and St. John's, Newfoundland, Canada A1C 5S7.
2
5Department of Mathematics, FUT, Owerri,Nigeria.
3
2Department of Biology, Memorial University of Newfoundland and St. John's, Newfoundland, Canada A1C 5S7.
4
3Department of Computer Science, Memorial University of Newfoundland and St. John's, Newfoundland, Canada A1C 5S7.
5
4Department of Mathematics and Statistics,Memorial University of Newfoundland and St. John's, Newfoundland, Canada A1C 5S7.
6
Department of Computer Science and Engineering, Michigan State University, East Lansing MI 48824.

Abstract

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. Variation in base composition of and (or) order within oligos can affect accuracy and (or) confidence of base calls. To evaluate the effect of order, we present oligos as n-gram neural input vectors of degree 3 and measure their performance. Microarray signal intensity data were divided into training, validation and testing sets. Regression values obtained were >99.80% overall with very low mean square errors that transform to high best validation performance values. Pattern recognition results showed high percentage confusion matrix values along the diagonal and receiver operating characteristic curves were clustered in the upper left corner, both indices of good predictive performance. Higher order n-grams are expected to produce even better predictions.

KEYWORDS:

Confusion matrix; Neural networks; Performance; Receiver Operating Characteristic curves; Regression values; n-grams

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