Machine learning techniques applied to biomolecular data prediction
DOI:
https://doi.org/10.33448/rsd-v15i3.50648Keywords:
Machine learning, Deep learning, AI.Abstract
The objective of this study was to analyze the application of machine learning (ML) techniques in the evaluation of biomolecular prediction data in the genetic field, comparing different approaches regarding accuracy, efficiency, and applicability in the analysis of biomolecular data, as well as identifying and discussing the main limitations and advances of these techniques. In the era of Industry 4.0, there is a significant growth in the volume of digital data, including health information, social media, and the Internet of Things, whose analysis demands the use of artificial intelligence (AI), especially ML and deep learning (DL). AI enables computational systems to perform tasks traditionally associated with human intelligence, being widely applied to the analysis of big data in healthcare. Among its advantages are flexibility, scalability, and the ability to integrate heterogeneous data, such as demographic information, laboratory tests, images, and clinical texts, contributing to diagnosis, prognosis, and risk prediction. ML has wide application in molecular biology and drug discovery, while DL, based on deep neural networks, proves particularly efficient in analyzing large volumes of data, such as in cancer diagnosis and the identification of therapeutic targets. However, challenges persist, including the need for robust computational infrastructure, limited interpretability of models, high computational costs, and ethical issues related to data privacy and security.
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