Genome sequencing using machine learning with a special focus on tuberculosis
Keywords:
mayobacterium tuberculosis, genome sequencing, machine learningAbstract
Machine learning is becoming increasingly prevalent. However, in the discipline of Bioinformatics and Computational Biology, it is not a popular use case. Machine learning techniques are used in only a few technologies. The majority of the tools are built using deterministic techniques and algorithms. Deoxyribonucleic acid (DNA) is a biological macromolecule composed up of deoxyribonucleic acid. Its main function is to store data. Due to breakthroughs in sequencing technology, DNA sequence data is presently rising at an exponential pace, ushering the study of DNA sequences into the big data age. Machine learning is also a powerful tool for massive processing it learns on its own from large volumes of data. We've talked about machine learning techniques and how they can be used to improve genome sequencing accuracy. In our review we have also discussed about genome sequence for Mycobacterium Tuberculosis. Tuberculosis is because of the bacteria, Tuberculosis caused by Mycobacterium tuberculosis. TB is considered one of the leading the reasons for dying all over the world. MDR-TB is a form of germs that cause tuberculosis that is not susceptible to anti-TB medications such as isoniazid (INH) and rifampin (RMP).
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