DNA sequence classification
U-net architecture
Deep learning
Genomics
Sequence data
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The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized
medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to
breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene.
Conventional machine learning techniques often necessitate intricate feature engineering and may not capture
the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet
architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention
mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences
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