Computing techniques for the analysis of massive DNA sequencing in personalized medicine
DOI:
https://doi.org/10.18041/1794-4953/avances.1.12584Keywords:
Genome, biotechnology, health, molecular biology, Computational techniquesAbstract
The aim of this bibliographic research is to explore in depth the various computational techniques used for the analysis of massive DNA sequencing in the context of personalized medicine. Both traditional approaches and recent innovations in this field are examined, highlighting their applications, advantages, and limitations. Furthermore, emerging research areas at the intersection of computer science and genomics are addressed, aiming to foster continuous progress in delivering personalized and precise healthcare. The study emphasizes the need for a multidisciplinary approach that ensures patient data privacy to achieve accurate diagnoses and fully harness advances in DNA analysis. Technologies such as Machine Learning, Deep Learning, and Artificial Intelligence play a fundamental role in managing and analyzing large volumes of genomic data, enabling the integration of information that provides a more comprehensive view of biological systems. Additionally, mechanistic, metabolic, and quantitative models are also analyzed, aimed at personalizing treatments for diseases such obesity, cancer, and Alzheimer’s disease.
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