Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

نویسندگان

چکیده

Biological pairwise sequence alignment can be used as a method for arranging two biological characters to identify regions of similarity. This operation has elicited considerable interest due its significant influence on various critical aspects life (e.g., identifying mutations in coronaviruses). Sequence over large databases cannot yield results within reasonable time, power, and cost. heuristic methods, such FASTA, the BLAST family have been demonstrated perform 40 times faster than DP-based Needleman-Wunsch) techniques they guarantee an optimum result An optimized software platform widely DNA algorithm called Needleman-Wunsch (NW) based lookup table, is described this study. global best approach similar between sequences. study presents new application classical machine learning (ML) alignment. Customized ML models are implement NW accuracy 99.7% achieved when using multilayer perceptron with ADAM optimizer, up 2912 Giga cell updates per second realized real sequences length 4.1 M nucleotides. Our implementation valid RNA/DNA aims parallelize computation steps involved accelerate performance by algorithms. All datasets available from https://ieee-dataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3100408