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Experimental Result Paper | Data & Knowledge Engineering | India | Volume 12 Issue 10, October 2023 | Rating: 5 / 10
An Optimization Approach for Multiple Motifs Discovery in DNA Sequence Using LVCGRO
Abstract: Extracting regulatory motifs from the DNA sequence seems to increase with intense enthusiasm. The existing motif discovery models failed to cover multiple motifs that occurred in DNA sequence, did not run the risk of getting stuck in a local optimum, and were time-consuming in handling dozens of sequences. Therefore, in this work, an approach based on multi-objective optimization is used for motif discovery. Initially, the input DNA sequences are preprocessed to remove the inconsistencies and divide the lengthy sequence into shorter lengths. After that, the gram-tree representation is used to find all motifs effectively in the target sequence. The resultant output in the form of the alphanumeric DNA sequence is converted into the numerical form using the OH-ECE, and the MNBIRCH is used to categorize the motifs DNA sequence. Finally, to filter out the optimal motifs present in the DNA sequence, LVCGRO is used. Experimental results show that the proposed method is successful in discovering the multiple motifs from the DNA sequence with a high accuracy of 98.561%.
Keywords: gram-tree construction, DNA sequence, Linearly Varying Constricted Gold Rush Optimization (LVCGRO), Multi-dimensional Nested Balanced Iterative Reducing And Clustering Using Hierarchies (MNBIRCH), One Hot Entropy-weight Correlated Encoding (OH-ECE), kmers Percent identify mean
Edition: Volume 12 Issue 10, October 2023,
Pages: 904 - 912