Machine Learning Techniques for Accurate Prediction of Proteins Function
DOI:
https://doi.org/10.63995/HIXN4599Keywords:
Bioinformatics; Deep Learning; Drug Discovery; Genomic Sequences; Machine Learning; Protein FunctionAbstract
Machine learning techniques are revolutionizing the prediction of protein functions, offering unprecedented accuracy and efficiency in understanding biological processes. By leveraging large datasets of protein sequences and structures, machine learning models can identify patterns and relationships that are often elusive to traditional methods. Techniques such as deep learning, support vector machines, and random forests have shown remarkable success in predicting protein functions, including enzymatic activities, binding sites, and interaction networks. These approaches utilize diverse data sources, including genomic sequences, structural annotations, and interaction data, to train models capable of making precise functional predictions. The integration of machine learning with bioinformatics tools accelerates the discovery of novel protein functions and facilitates the annotation of uncharacterized proteins, contributing significantly to fields like drug discovery, disease diagnosis, and synthetic biology. This abstract underscores the transformative impact of machine learning on protein function prediction, highlighting its potential to enhance our understanding of complex biological systems and drive advancements in biomedical research.
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