Motif Discovery in DNA Sequences Using Scaled Conjugate Gradient Neural Networks
DOI:
https://doi.org/10.32792/jeps.v13i1.248Keywords:
Bioinformatics, Data Mining, Deoxyribonucleic Acid (DNA), Motif Discovery, Artificial Neural Networks (ANNs), SCGAbstract
Finding motifs in DNA sequences is a current challenge and an essential step in bioinformatics.
Processing these issues needs considerable data analysis due to technical advancements in the
industry. Artificial Neural Networks (ANNs) are increasingly used, particularly for motif
identification and genomic analysis. In order to find motifs in DNA sequences, this work proposed a
supervised learning algorithm for feed-forward neural networks called Scaled Conjugate Gradient
(SCG) algorithm. The SCG algorithm utilizes a step-size scaling mechanism that is fully automated to
minimize time-consuming row searches during each training iteration. This algorithm was used in this
work for motif discovery to train code patterns and to reduce a multivariate global error function
dependent on the network weights. It trains many code patterns of lengths between 4 to 509 bases to
find them in a database with 2,227,382 bases; many experiments were done with different numbers of
hidden layers; our finding ten hidden layers provide the best results, with training percentage is 100%.
Compared to the other supervised learning neural network algorithms, One Step Secant, Gradient
Descent, Bayesian Regularization, and BFGS Quasi-Newton; our find SCG algorithm produced
higher accuracy (100%) and less time during the training and testing phases.
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