Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this\nevolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more\nefficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools\nis machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure\nevolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the\nprediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts\nthe genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes\nbased on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains,\nthen a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is\napplied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from\ntwo sources are used in the validation of these techniques. The results show that the accuracy of this technique in\npredicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis\nof the correlation between different nucleotides in the same RNA sequence.
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