Improving Dna Genetic Code Similarity Evaluation That Uses Machine Learning and the Enhanced Longest Common Subsequence (Elcs) Algorithm

Applications of DNA sequence analysis in biology and computer science, including gene discovery, evolution research, and genetic illness diagnosis. Finding commonalities across codes in various domains is essential. When comparing DNA sequences, conventional techniques like the subsequence (LCS) algorithm are frequently employed. But in order to clarify the difficulties in assessing DNA similarity sequence, this research study presents a method that makes use of machine learning techniques and the Enhanced Longest Common Subsequence Algorithm (ELCS). The suggested ELCS algorithm combines the strength of datadriven models with the efficiency of sequence alignment. It predicts alignment scores using a trained machine learning model, which lessens workload while keeping accuracy high. The Enhanced LCS Algorithm (ELCS) was implemented and deployed with Support Vector Machines using the NCBI GenBank nucleotide sequence dataset.