Efficient DNA Sequence Identification: A Classification Approach Integrating Gaussian RBM

Using machine learning techniques, this article examines cancer patients' DNA sequences. Cancer is certainly one of the most common and devastating diseases in the contemporary era, with a large number of new cases and deaths every year. Many people lose their lives each year to this terrible illness. Methods to combat this illness are the focus of intense investigation by scientists from a wide range of fields. Recently, machine learning has improved the analysis of cancer patients' DNA sequences. Sequencing is crucial for preprocessing, feature extraction, and model training. Among the preprocessing stages is cleaning up the sequence, which entails eliminating PCR repetitions, impurities, and vectors. Feature extraction has been carried out on each DNA sequence in order to retrieve statistical characteristics. It is of utmost importance to regulate features accurately when training a Gaussian RBM. A grade of 96.44% demonstrated a considerable improvement in accuracy, according to the results.