The Effect of AI On DNA Sequencing An Overview of The Process Between History and Present With ethical analysis

Abstract

The expense of DNA sequencing methods has fallen by many orders of magnitude over the previous decade, with a commensurate improvement in speed and capacity. It took around 15 years for the Human Genome Project to finalize the human genome sequence with the advent of the second and third-generation technology, the process of DNA sequencing can now be accomplished in just a matter of days or even hours. This has resulted in a significant increase in the accessibility of sequencing tools to researchers, leading to the replacement of traditional approaches with DNA sequencing and opening up new avenues of research. DNA sequencing is crucial for understanding fundamental biological processes and is expected to become increasingly important in various fields of medicine (preimplantation diagnostics, cancer, infectious diseases). Current estimates place the cost of sequencing one human genome at approximately ${\$}$1,000. Without a doubt, we may expect advancements in current technologies as well as the progress of fourth-generation technologies in the upcoming decades. Artificial intelligence (AI), on the other hand, plays a substantial role in the analysis and interpretation of DNA sequence data. DNA sequencing creates massive volumes of data that might be difficult to understand without computational tools. Machine learning and deep learning AI techniques may be used to evaluate and understand this data, resulting in new insights and discoveries. One key use of AI in DNA sequencing is the identification of genetic variants linked to medical conditions. Machine learning algorithms can uncover patterns in data that are predictive of illness risk by evaluating massive datasets of DNA sequences from persons with and without a certain condition. This knowledge may subsequently be applied to the development of unique diagnostic tests and therapies.

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